Research

Google Scholar

Books and Book Chapters

D. Marman, F. Khalvati, M. Shafaei. “The Hidden Teachings of Rumi.” 324 pages, Spiritual Dialogues Project, 2019. Online Link
F. Khalvati, Y. Zhang, A. Wong, M. A. Haider. “Radiomics.” Encyclopedia of Biomedical Engineering, vol. 2, pp. 597-603. Elsevier, 2019. Online Link

Peer-Reviewed Journal Articles (Published)

Zhou, M., Wagner, M.W., Kudus, K., Hawkins, C., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. Generating 3D brain tumor regions in MRI using vector-quantization generative adversarial networks (2024) Computers in Biology and Medicine.
Gong, B., Khalvati, F., Ertl-Wagner, B.B., Patlas, M. Artificial Intelligence in emergency neuroradiology: Current applications and perspectives (2024) Diagnostic and Interventional Imaging.
Namdar, K., Wagner, M.W., Kudus, K., Hawkins, C., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. Improving deep learning models for pediatric low-grade glioma tumours molecular subtype identification using MRI-based 3D probability distributions of tumour location (2024) Canadian Association of Radiologists Journal.
Rogalla, P., Fratesi, J., Kandel, S., Patsios, D., Khalvati, F., Carey, S. Development and evaluation of an automated protocol recommendation system for chest CT using natural language processing with CLEVER terminology word replacement (2024) Canadian Association of Radiologists Journal.
Kudus, K., Wagner, M.W., Namdar, K., Bennett, J., Nobre, L., Tabori, U., Hawkins, C., Ertl-Wagner, B.B., Khalvati, F. Beyond hand-crafted features for pretherapeutic molecular status identification of pediatric low-grade gliomas (2024) Scientific Reports.
Kudus, K., Wagner, M.W., Ertl-Wagner, B.B., Khalvati, F. Applications of machine learning to MR imaging of pediatric low-grade gliomas (2024) Child’s Nervous System.
Boutet, A., Haile, S.S., Yang, A.Z., Son, H.J., Malik, M., Pai, V., Nasralla, M., Germann, J., Vetkas, A, Khalvati, F., Ertl-Wagner, B.B. Assessing the emergence and evolution of Artificial Intelligence and machine learning research in neuroradiology (2024) American Journal of Neuroradiology.
Soldatelli, M.D., Wagner, M.W., Namdar, K., Tabori, U., Hawkins, C., Khalvati, F., Ertl-Wagner, B.B. Multiclass pediatric low-grade neuroepithelial tumor molecular subtype identification with bi-institutional ADC MRIs and machine Learning (2024) American Journal of Neuroradiology.
Stott, S.M., Wu, Y., Hosseinpour, S., Chen, C., Namdar, K., Amirabadi, A., Man, C., Shroff, M., Khalvati, F.,Doria, A. A correlative assessment of machine learning-based cobb angle measurements and human-based measurements in adolescent idiopathic and congenital scoliosis (2024) Canadian Association of Radiologists Journal.
Bhatia, A., Khalvati, F., Ertl-Wagner, B.B. Artificial Intelligence in the future landscape of pediatric neuroradiology: opportunities and challenges. American Journal of Neuroradiology, 2024. Online Link
Kudus, K., Wagner, M.W., Namdar, K., Nobre, L., Bouffet, E., Tabori, U., Hawkins, C., Yeom, K.W., Ertl-Wagner, B.B., Khalvati, F. Increased confidence of radiomics facilitating pretheraputic differentiation of BRAF-altered pediatric low-grade glioma. European Radiology, 2024. Online Link
Dixon, K., Bonon, R., Ivander, F., Ale Ebrahim, S., Namdar, K., Shayegannia, M., Khalvati, F., Kherani, N.P., Zavodni, A., Matsuura, M. Using machine learning and silver nanoparticle-based surface-enhanced Raman spectroscopy for classification of cardiovascular disease biomarkers. ACS Applied Nano Materials, 2023. Online Link
Vafaeikia, P., Wagner, M.W., Hawkins, C., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. End-to-end pediatric low-grade glioma segmentation and classification. Canadian Association of Radiologists Journal, 2023. Online Link
Liu, X., Espin-Garcia, O., Khalvati, F., Namdar, K., Fischer, S., Haider, M.A., Jhaveri, K.S. Hepatocellular adenoma subtyping by qualitative magnetic resonance imaging features and hepatocellular adenoma subtyping by qualitative MRI features and machine learning algorithm of integrated qualitative and quantitative features: a proof-of-concept study. Clinical Radiology, 2023. Online Link
Wagner, M.W., Nobre, L., Namdar, K., Khalvati, F., Tabori, U., Hawkins, C., Ertl-Wagner, B.B. T2-FLAIR mismatch sign in pediatric low-grade glioma. American Journal of Neuroradiology, 2023. Online Link
Akbarian, S., Seyyed-Kalantari, L., Khalvati, F., Dolatabadi, E. Evaluating knowledge transfer in neural network for medical images. IEEE Access, 2023. Online Link
Wu, Y., Namdar, K., Chen, C., Hosseinpour, S., Shroff, M., Doria, A., Khalvati, F. Automated adolescence scoliosis detection using Augmented U-Net with non-square kernels. Canadian Association of Radiologists Journal, 2023. Online Link
Taheri-Shirazi, M., Namdar, K., Ling, K., Karmali, K., McCradden, M.D., Lee, W., Khalvati, F. Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning. Frontiers in Public Health, section Digital Public Health, 2023. Online Link
Rajapaksa, S., Khalvati, F. Relevance maps: a weakly-supervised segmentation method for 3D brain tumour in MRIs. Frontiers in Radiology, section Artificial Intelligence in radiology, 2022. Online Link
Jin, H., Wagner, M.W., Ertl-Wagner, B.B., Khalvati, F. An educational graphical user interface to construct convolutional neural networks for teaching artificial intelligence in radiology. Canadian Association of Radiologists Journal, 2022. Online Link
Watanabe, A., Ketabi, S., Namdar, K., Khalvati, F. Improving disease classification performance and explainability of deep learning models in radiology with heatmap generators. Frontiers In Radiology, section Artificial Intelligence in Radiology, 2022. Online Link
Leung, Y.T., Khalvati, F. Exploring COVID-19 related stressors using topic modeling. Journal Of Medical Internet Research, 2022. Online Link
Wagner, M.W., Namdar, K., Napoleone, M., Hainc, N., Amirabadi, A., Fonseca, A., Laughlin, S., Shroff, M., Bouffet, E., Hawkins, C., Khalvati, F., Bartels, U., Ertl-Wagner, B.B. Radiomic features based on MRI predict progression-free survival in pediatric diffuse midline glioma / diffuse intrinsic pontine glioma. Canadian Association of Radiologists Journal 2022. Online Link
Wagner, M.W., Namdar, K., Alqabbani, A., Hainc, N., Nobre Figuereido, L., Sheng, M., Shroff, M.M., Bouffet, E., Tabori, U., Hawkins, C., Zhang, M., Yeom, K.W., Khalvati, F., Ertl-Wagner, B.B. Dataset size sensitivity analysis of machine learning classifiers to differentiate molecular markers of paediatric low-grade gliomas based on MRI. Oncology And Radiotherapy, 2022. Online Link
Ertl-Wagner, B.B., Khalvati, F. The data behind the image – Deep-Learning and its potential impact in neuro-oncological imaging. Neuro-Oncology, 2021. Online Link
Monah, S., Wagner, M.W., Biwas, A., Khalvati, F., Erdman, L., Amirabadi, A., Vidarsson, L., McCradden, M., Ertl-Wagner, B.B. Data governance functions to support responsible data stewardship in pediatric radiology research studies using Artificial Intelligence. Pediatric Radiology, 2022. Online Link
Motamed, S., Rogalla, P., Khalvati, F. Data augmentation using generative adversarial networks (GANs) for GAN-based detection of pneumonia and COVID-19 in chest X-Ray images. Informatics in Medicine Unlocked, 2021. Online Link
Wagner, M.W., Namdar, K., Biswas, A., Monah, S., Khalvati, F., Ertl-Wagner, B.B. Radiomics, machine learning, artificial intelligence – What the neuroradiologist needs to know. Neuroradiology, 2021. Online Link
Hao, R., Namdar, K., Liu, I.L., Khalvati, F. A transfer learning based active learning framework for brain tumor classification. Frontiers in Artificial Intelligence, 2021. Online Link
Motamed, S., Rogalla, P., Khalvati, F. RANDGAN: Randomized generative adversarial network for detection of covid-19 in chest X-Ray. Scientific Reports, 11, 8602, 2021. Online Link
Namdar, K., Haider, M.A., Khalvati, F. A modified AUC for training convolutional neural networks: taking confidence into account (2021) Frontiers in Artificial Intelligence, 2021. Online Link
Hao, R., Namdar, K., Liu, I.L., Haider, M.A., Khalvati, F. A comprehensive study of data augmentation strategies for prostate cancer detection in diffusion weighted MRI using convolutional neural networks. Journal of Digital Imaging, 2021. Online Link
Zhang, Y., M. Lobo-Mueller, E., Karanicolas, P., Gallinger, S., Haider, M.A., Khalvati, F. Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images. Scientific Reports, 11, 1378, 2021. Online Link
Wagner, M.W., Hainc, N., Khalvati, F., Namdar, K., Figureido, L., Sheng, M., Amirabadi, A., Laughlin, S., Shroff, M., Tabori, U., Hawkins, C., Yeom, K., Ertl-Wagner, B.B. Radiomics of pediatric low grade gliomas: toward a pretherapeutic differentiation of BRAF-mutated and BRAF-fused tumors. American Journal of Neuroradiology, 11;42(4):759-765, 2021. Online Link
Liu, X., Khalvati, F., Namdar, K., Fischer, S., Lewis, S., Taouli, B., Haider, M.A., Jhaveri, K.S. Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning? European Radiology, 31(1):244-255, 2021. Online Link
Salinas-Miranda, E., Deniffel, D., Dong, X., Healy, G.M., Khalvati, F., O’Kane, G.M., Knox, J., Bathe, O.F., Baracos, V.E., Gallinger, S., Haider, M.A. Prognostic value of early changes in CT measured body composition in patients receiving chemotherapy for unresectable pancreatic cancer. European Radiology, 2021. Online Link
Ivanics, T., Salinas- Miranda, E., Abreu, P., Khalvati, F., Namdar, K., Dong, X., Deniffel, D., Gorgen, A., Erdman, L., Jhaveri, K., Haider, M.A., Veit-Haibach, P., Sapisochin, G. A pre-tace radiomics model to predict HCC progression and recurrence in liver transplantation. A pilot study on a novel biomarker. Transplantation, 2021. Online Link
Salinas-Miranda, E., Khalvati, F., Namdar, K., Deniffel, D., Dong, X., Abbas, E., Wilson, J.M., O’Kane, G.M., Knox, J., Gallinger, S., Haider, M.A. Validation of prognostic radiomic features from resectable pancreatic ductal adenocarcinoma in patients with advanced disease undergoing chemotherapy. Canadian Association of Radiologists Journal, 2020. Online Link
Zhang, Y., M. Lobo-Mueller, E., Karanicolas, P., Gallinger, S., Haider, M.A., Khalvati, F. Prognostic value of transfer learning based features in resectable pancreatic ductal adenocarcinoma (2020) Frontiers in Artificial Intelligence – Medicine and Public Health. Frontiers in Artificial Intelligence – Medicine and Public Health, 2020. Online Link
Deniffel, D., Abraham, N., Namdar, K., Dong, X., Salinas, E., Milot, L., Khalvati, F., Haider, M.A. Using decision curve analysis to benchmark performance of a magnetic resonance imaging–based deep learning model for prostate cancer risk assessment. European Radiology, 2020. Online Link
Zhang, Y., Lobo-Mueller, E.M., Karanicolas, P., Gallinger, S., Haider, M.A., Khalvati, F. CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging. BMC Medical Imaging, 20(11), 2020. Online Link
Dulhanty, C., Wang, L., Cheng, M., Gunraj, H., Khalvati, F., Haider, M.A., Wong, A. Radiomics driven diffusion weighted imaging sensing strategies for zone-level prostate cancer sensing. Sensors, 2020. Online Link
Deniffel, D., Zhang, Y., Salinas, E., Satkunasivam, R., Khalvati, F., Haider, M.A. Reducing unnecessary prostate multiparametric magnetic resonance imaging by using clinical parameters to predict negative and indeterminate findings. The Journal of Urology, Vol. 203, No. 2, 2020. Online Link
Yoo, S., Gujrathi, I., Haider, M.A., Khalvati, F. Prostate cancer detection using deep convolutional neural networks. Scientific Reports, 9, 19518, 2019. Online Link
Khalvati, F., Zhang, Y., Baig, S., Lobo-Mueller, E.M., Karanicolas, P., Gallinger, S., Haider, M.A. Prognostic value of CT radiomic features in resectable pancreatic ductal adenocarcinoma. Scientific Reports, 9, 5449, 2019. Online Link
Zhang, M., Milot, L., Khalvati, F., Sugar, L., Downes, M., Baig, S.M., Klotz, L., Haider, M.A. Value of increasing biopsy cores per target with cognitive MRI-targeted transrectal US prostate biopsy. Radiology, Vol. 291, No. 1, 2019. Online Link
Khalvati, F., Zhang, J., Chung, A.G., Shafiee, M.J., Wong, A., Haider, M.A. MPCaD: A multi-scale radiomics-driven framework for automated prostate cancer localization and detection. BMC Medical Imaging, 18:16, 2018. Online Link
Oikonomou, A., Khalvati, F., Tyrrell, P.N., Haider, M.A., Tarique, U., Jimenez-Juan, L., Tjong, M.C., Poon, I., Eilaghi, A., Ehrlich, L., Cheung, P. Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy. Scientific Reports, 8, 4003, 2018. Online Link
Zhang, Y., Oikonomou, A., Wong, A., Haider, M.A., Khalvati, F. Radiomics-based prognosis analysis for non-small cell lung cancer. Scientific Reports, 7, 46349, 2017. Selected Among Editor’s Choice in Sep 2019. Online Link
Clark, T., Zhang, J., Baig, S., Wong, A., Haider, M., Khalvati, F. Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks. Journal of Medical Imaging, Vol. 4, No. 4:041307, 2017. Online Link
Shafiee, M.J., Chung, A.G., Khalvati, F., Haider, M.A., Wong, A. Discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection. Journal of Medical Imaging, Vol. 4, No. 4:041305, 2017. Online Link
Eilaghi, A., Baig, S., Zhang, Y., Zhang, J., Karanicolas, P., Gallinger, S., Khalvati, F., Haider, M.A. CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma – a quantitative analysis. BMC Medical Imaging, 2017. Online Link
Haider, M.A., Vosough, A., Khalvati, F., Kiss, A., Ganeshan, B., Bjarnason, G.A. CT texture analysis: A potential tool for prediction of survival in patients with metastatic clear cell carcinoma treated with sunitinib. BMC Cancer Imaging, 2017. Online Link
Khalvati, F., Salmanpour, A., Rahnamayan, S., Haider, M.A., Tizhoosh, H.R. Sequential registration-based segmentation of the prostate gland in MR image volumes. Journal of Digital Imaging, Vol. 29, pp. 254-263, 2016. Online Link
Cameron, A., Khalvati, F., Haider, M.A., Wong, A. MAPS: A quantitative radiomics approach for prostate cancer detection. IEEE Transactions on Biomedical Engineering, Vol. 63, No. 6, pp. 1145-1156, 2016. Online Link
Li, E., Khalvati, F., Shafiee, M.J., Haider, M.A., Wong, A. Sparse reconstruction of compressive sensing MRI using cross-domain stochastically fully connected conditional random fields. BMC Medical Imaging, 2016. Online Link
Boroomand, A., Shafiee, M.J., Khalvati, F., Haider, M.A., Wong, A. Noise-compensated, bias-corrected diffusion weighted endorectal magnetic resonance imaging via a stochastically fully-connected joint conditional random field model. IEEE Transactions on Medical Imaging, Vol. 35, No. 12, pp. 2587-2597, 2016. Online Link
Khalvati, F., Gallego-Ortiz, C., Balasingham, S., Martel, A.L. Automated segmentation of breast in 3-D MR images using a robust atlas. IEEE Transactions on Medical Imaging, Vol. 34, No. 1, pp. 116-125, 2015. Online Link
Khalvati, F., Wong, A., Haider, M.A. Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models. BMC Medical Imaging, pp. 15-27, 2015. Online Link
Khalvati, F., Aagaard, M.D., Tizhoosh, H.R. Window memoization: Toward high-performance image processing software. Journal of Real-Time Image Processing, Vol. 10, No. 1, pp. 5-25, 2015. Online Link
Chung, A.G., Khalvati, F., Shafiee, M.J., Haider, M.A., Wong, A. Prostate cancer detection via a quantitative radiomics-driven conditional random field framework. IEEE Access, Vol. 3, pp. 2531-2541, 2015. Online Link
Othman, A.A., Tizhoosh, H.R., Khalvati, F. EFIS-evolving fuzzy image segmentation. IEEE Transactions on Fuzzy Systems, Vol. 22, No. 1, pp. 72-82, 2014. Online Link
Khalvati, F., Salmanpour, A., Rahnamayan, S., Rodrigues, G., Tizhoosh, H.R. Inter-slice bidirectional registration-based segmentation of the prostate gland in MR and CT image sequences. Medical Physics, Vol. 40, No.12, pp.123503-1-11, 2013. Online Link
Martin, S., Rodrigues, G., Patil, N., Bauman, G., D’Souza, D., Sexton, T., Palma, D., Louie, A.V., Khalvati, F., Tizhoosh, H.R., Gaede, S. A multiphase validation of atlas-based automatic and semiautomatic segmentation strategies for prostate MRI. International Journal of Radiation Oncology, Biology, Physics. Vol. 85, No. 1, pp. 95-100, 2013. Online Link
Khalvati, F., Aagaard, M.D. Window memoization: An efficient hardware architecture for high-performance image processing. Journal of Real-Time Image Processing, Vol. 5, No. 3, pp. 195-212, 2010. Online Link
Baroughi, M.F., Jeyakumar, R., Vygranenko, Y., Khalvati, F., Sivoththaman, S. Fabrication and characterization of amorphous Si/crystalline Si heterojunction devices for photovoltaic applications. Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films, Vol. 22, No. 3, pp. 1015-1019, 2004. Online Link

Peer-Reviewed Full-Length Papers in Conference Proceedings (Published)

Zhou, M., Zhang, Y., Xu, X., Wang, J., Khalvati, F. Edge-enhanced dilated residual attention network for multimodal medical image fusion (2024) Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM).
Zhao, C., Kudus, K., Ketabi, S., Namdar, K., Wagner, M.W., Ertl-Wagner, B.B., Khalvati, F. Improving interpretability of radiology report-based pediatric brain tumor pathology classification and key-phrases extraction using large language models (2024) Proceedings of IEEE-EMBS International Conference on Biomedical and Health (BHI).
Namdar, K., Khalvati, F. Advanced receiver operating characteristic curve analysis to identify outliers in binary machine learning classifications for precision medicine (2024) Proceedings of IEEE-EMBS International Conference on Biomedical and Health (BHI).
Chen, C., Namdar, K., Wu, Y., Hosseinpour, S., Shroff, M., Doria, A., Khalvati, F. Automating cobb angle measurement for adolescent idiopathic scoliosis using instance segmentation (2024) Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
Chen, C., Namdar, K., Wagner, M.W., Ertl-Wagner, B.B., Khalvati, F. Anomaly detection in pediatric and adults brain MRI with generative Model (2024) Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
Zhou, M., Khalvati, F. Conditional generation of 3D brain tumor regions via VQGAN and temporal-agnostic masked transformer (2024) Proceedings of Machine Learning Research.
Sadatamin, S., Ketabi, S., Donszelmann-Lund, E., Abtahi, S., Chaban, Y., Robbins, S., Tyc, R., Khalvati, F., Waspe, A.C., Kahrs, L.A., Drake, J.M. Enhancing MR-guided laser interstitial thermal therapy planning using U-Net: a data-driven approach for predicting MR thermometry images (2024) Proceedings of the International Society for Optics and Photonics, SPIE Medical Imaging.
Rajapaksa, S., Namdar, K., Khalvati, F. Combining weakly supervised segmentation with multitask learning for improved 3D MRI brain tumour classification. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshops, 2023. Online Link
Ketabi, S., Agnihotri, P., Zakeri, H., Namdar, K., Khalvati, F. Multimodal learning for improving performance and explainability of chest X-ray classification. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshops, 2023. Online Link
Rajapaksa, S., Vianney, J.M.U, Castro, R., Khalvati, F., Aich, S. Using large text to image models with structured prompts for skin disease identification. International Conference on Computer Vision (ICCV) Workshops, 2023. Online Link
Rajapaksa, S., Khalvati, F. Optimized global perturbation attacks for brain tumour ROI extraction from binary classification models. International Conference on Neural Information Systems (NeurIPS) Workshops, 2022. Online Link
Vafaeikia, P., Wagner, M.W., Tabori, U., Hawkins, C., Ertl-Wagner, B.B., Khalvati, F. Improving the segmentation of pediatric low-grade gliomas through multi-task learning. International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2022. Online Link
Rajapaksa, S., Khalvati, F. Localized perturbations for weakly supervised segmentation of glioma brain tumours. International Conference on Neural Information Systems (NeurIPS) Workshops. 2021. Online Link
Motamed, S., Khalvati, F. Multi-class Generative Adversarial Networks: Improving one-class classification of pneumonia using limited labeled data. International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021. Online Link
Motamed, S., Khalvati, F. Inception-GAN for semi-supervised detection of pneumonia in chest X-rays. International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021. Online Link
Motamed, S., Rogalla, P., Khalvati, F. RANDGAN: Randomized generative adversarial network for detection of COVID-19 in Chest X-Ray. International Conference on Neural Information Systems (NeurIPS) Workshops, 2020. Online Link
Akbarian, S., Seyyed-Kalantari, L., Khalvati, F., Dolatabadi, E. Attention transfer outperforms transfer learning in medical image disease classifiers. International Conference on Neural Information Systems (NeurIPS) Workshops, 2020. Online Link
Namdar, K., Gujrathi, I., Haider, M.A., Khalvati, F. Evolution-based fine-tuning of CNNs for prostate cancer detection. International Conference on Neural Information Systems (NeurIPS) Workshops, Vancouver, BC, Canada, 2019. Among Top 5 Submissions. Online Link
Khalvati, F., Zhang, Y., Le, P.H.U., Gujrathi, I., Haider, M.A. PI-RADS guided discovery radiomics for characterization of prostate lesions with diffusion-weighted MRI. SPIE Medical Imaging, San Diego, CA, USA, 2019. Online Link
Hu, X., Chung, A.G., Fieguth, P., Khalvati, F., Haider, M.A., Wong. ProstateGAN: Mitigating data bias via prostate diffusion imaging synthesis with generative adversarial networks. International Conference on Neural Information Systems (NeurIPS) Workshops, Montreal, QC, Canada, 2018. Online Link
Khalvati, F., Zhang, J., Wong, A., Haider, M.A. Bag of bags: Nested multi instance classification for prostate cancer detection. IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, USA, 2016. Online Link
Clark, T., Wong, A., Haider, M.A., Khalvati, F. Fully deep convolutional neural networks for segmentation of the prostate gland in diffusion-weighted MR images. 14th International Conference on Image Analysis and Recognition (ICIAR), Montreal, QC, Canada, 2017. Online Link
Cho, D.S., Khalvati, F., Clausi, D.A., Wong, A. A machine learning-driven approach to computational physiological modeling of skin cancer. 14th International Conference on Image Analysis and Recognition (ICIAR), Montreal, QC, Canada, 2017. Online Link
Zhang, J., Baig, S., Wong, A., Haider, M.A., Khalvati, F. Segmentation of prostate in diffusion MR images via clustering. 14th International Conference on Image Analysis and Recognition (ICIAR), Montreal, QC, Canada, 2017. Online Link
Kumar, D., Chung, A.G., Shafiee, M.J., Khalvati, F., Haider, M.A., Wong, A. Discovery radiomics for pathologically-proven computed tomography lung cancer prediction. 14th International Conference on Image Analysis and Recognition (ICIAR), Montreal, QC, Canada, 2017. Online Link
Karimi, A.H., Chung, A.G., Shafiee, M.J., Khalvati, F., Haider, M.A., Ghodsi, A., Wong, A. Discovery radiomics via a mixture of deep ConvNet sequencers for multi-parametric MRI prostate cancer classification. 14th International Conference on Image Analysis and Recognition (ICIAR), Montreal, QC, Canada, 2017. Online Link
Yoo, S., Haider, M.A., Khalvati, F. Estimating optimal depth of VGG Net with Tree-structured Parzen estimators. Annual Conference on Vision and Intelligent Systems, Waterloo, ON, Canada, 2017. Online Link
Khalvati, F., Zhang, J., Baig, S., Haider, M.A., Wong, A. Sparse correlated diffusion imaging: A new computational diffusion MRI for prostate cancer detection. Annual Conference on Vision and Intelligent Systems, Waterloo, ON, Canada, 2016. Won Synaptive Best Imaging Paper Award. Online Link
Zhang, J., Baig, S., Wong, A., Haider, M.A., Khalvati, F. A local ROI-specific atlas-based segmentation of prostate gland and transitional zone in diffusion MRI. Annual Conference on Vision and Intelligent Systems, Waterloo, ON, Canada, 2016. Online Link
Khalvati, F., Zhang, J., Haider, M.A., Wong, A. Enhanced dual-stage correlated diffusion imaging. International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016. Online Link
Boroomand, A., Li, E., Shafiee, M.J., Haider, M.A., Khalvati, F., Wong, A. A unified Bayesian-based compensated magnetic resonance imaging. International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016. Online Link
Othman, A., Tizhoosh, H.R., Khalvati, F. Self-configuring and evolving fuzzy image thresholding. IEEE International Conference on Machine Learning and Applications (ICMLA), 2015. Online Link
Shafiee, M.J., Chung, A.G., Kumar, D., Wong, A., Khalvati, F., Haider, M.A. Discovery radiomics via StochasticNet sequencers for cancer detection. International Conference on Neural Information Systems (NIPS) Workshops, 2015. Online Link
Camlica, Z., Tizhoosh, H.R., Khalvati, F. Medical image classification via SVM using LBP features from saliency-based folded data. IEEE International Conference on Machine Learning and Applications (ICMLA), 2015. Online Link
Çamlica, Z., Tizhoosh, H.R., Khalvati, F. Autoencoding the retrieval relevance of medical images. IEEE International Conference on Image Processing, Tools, Theory and Applications (IPTA), 2015. Online Link
Wong, A., Khalvati, F., Haider, M.A. Dual-stage correlated diffusion imaging. IEEE International Symposium on Biomedical Imaging (ISBI), 2015. Online Link
Chung, A.G., Scharfenberger, C., Khalvati, F., Wong, A., Haider, M.A. Statistical textural distinctiveness in multi-parametric prostate MRI for suspicious region detection. International Conference on Image Analysis and Recognition (ICIAR), 2015. Online Link
Haider, S.A., Shafiee, M.J., Chung, A., Khalvati, F., Oikonomou, A., Wong, A., Haider, M.A. Single-click, semi-automatic lung nodule contouring using hierarchical conditional random fields (ISBI), 2015. Online Link
Khalvati, F., Modhafar, A., Cameron, A., Wong, A., Haider, M.A. A multi-parametric diffusion magnetic resonance imaging texture feature model for prostate cancer analysis. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshops, 2014. Online Link
Cameron, A., Modhafar, A., Khalvati, F., Lui, D., Shafiee, M.J., Wong, A., Haider, M.A. Multiparametric MRI prostate cancer analysis via a hybrid morphological-textural model. International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2014. Online Link
Khalvati, F., Martel, A. Atlas-based segmentation of breast MR images. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshops, 2013. Online Link
Khalvati, F., Tizhoosh, H.R. Perfect window memoization: A theoretical model of an optimization technique for image processing algorithms. International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), 2011. Online Link
Khalvati, F., Kianpour, M., Tizhoosh, H.R. Cascaded window memoization for medical imaging. International Conference on Artificial Intelligence Applications and Innovations (AIAI), 2011. Online Link
Khalvati, F., Tizhoosh, H.R., Hajian, A.R. Increasing computational redundancy of digital images via multiresolutional matching. International Conference on Image Analysis and Recognition (ICIAR), 2009. Online Link
Khalvati, F., Tizhoosh, H.R. An efficient architecture for hardware implementations of image processing algorithms. IEEE Symposium on Computational Intelligence for Image Processing (CIIP), 2009. Online Link
Khalvati, F., Aagaard, M.D., Tizhoosh, H.R. Accelerating image processing algorithms based on the reuse of spatial patterns. Canadian Conference on Electrical and Computer Engineering (CCECE), 2007. Online Link
Khalvati, F., Tizhoosh, H.R., Aagaard, M.D. Opposition-based window memoization for morphological algorithms. IEEE Symposium on Computational Intelligence in Image and Signal Processing (CIISP), 2007. Won IEEE Outstanding Student-Paper Travel Award. Online Link
Aagaard, M.D., Ciubotariu, V.C., Higgins, J.T., Khalvati, F. Combining equivalence verification and completion functions. Formal Methods in Computer-Aided Design (FMCAD), 2004. Online Link
Khalvati, F., Sivoththaman, S. Quantum efficiency modeling of amorphous/crystalline silicon photovoltaic devices. Material Research Society Symposium (MRS), 2003.

arXiv Manuscripts

Yoo, J.J., Namdar, K., Khalvati, F. Deep superpixel generation and clustering for weakly supervised segmentations of brain tumors in MR Images (2024). Online Link
Yoo, J.J., Namdar, K., McIntosh, C., Khalvati, F., Rogalla, P. A comprehensive study of radiomics-based machine learning for fibrosis detection (2023). Online Link
Yoo, J.J., Namdar, K., Wagner, M.W., Nobre, L., Tabori, U., Hawkins, C., Ertl-Wagner, B.B., Khalvati, F. A novel GAN-based paradigm for weakly supervised brain tumor segmentation of MR images (2023). Online Link
Namdar, K., Wagner, M.W., Ertl-Wagner, B.B., Khalvati, F. Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines (2023). Online Link
Namdar, K., Vafaeikia, P., Khalvati, F. Minimizing the effect of noise and limited dataset size in image classification using depth estimation as an auxiliary task with deep multitask learning (2022). Online Link
Zhang, Z., Khalvati, F. Introducing Vision Transformer for Alzheimer’s disease classification task with 3D input (2022). Online Link
S. Motamed, F. Khalvati. Vanishing Twin GAN: How Training a Weak Generative Adversarial Network Can Improve Semi-supervised Image Classification (2021) Online Link
P. Vafaeikia, K. Namdar, F. Khalvati. A Brief Review of Deep Multi-task Learning and Auxiliary Task Learning. (2020). Online Link
S. Motamed, I. Gujrathi, D. Deniffel, A. Oentoro, M. A. Haider, F. Khalvati. A Transfer Learning Approach for Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion Weighted MRI. (2019). Online Link

Conference 1-Page Abstracts

Ketabi, S., Wagner, M.W., Hawkins, C., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. Exploring the impact of radiology reports on MRI-based classification of pediatric brain tumor genetic markers (2024) Radiological Society of North America (RSNA). Chicago, Illinois, United States.
Ketabi, S., Wagner, M.W., Hawkins, C., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. Improving the explainability and performance of pediatric brain tumor molecular subtype classification through multimodal learning (2024) Radiological Society of North America (RSNA). Chicago, Illinois, United States.
Kudus, K., Wagner, M.W., Namdar, K., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. Segmentation-free pretherapeutic braf-status identification of pediatric low-grade gliomas (2024) Radiological Society of North America (RSNA). Chicago, Illinois, United States.
Hadi, A., Doria, A, Kim, D., Khalvati, F., Chen, C., Lebel, D.E.Enhancing accuracy in adolescent idiopathic scoliosis diagnosis in community hospitals: a comparative study of machine learning versus manual measurement of cobb angles (2024) Radiological Society of North America (RSNA). Chicago, Illinois, United States.
Namdar, K., Wagner, M.W., Sheng, M., Yeom, K.W., Hawkins, C., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. Confidence measurement for non-invasive pediatric low-grade glioma molecular subtype identification using Monte Carlo radiomics extraction with multi-class machine learning and bi-institutional MRI (2024) Nature Conference: Precision Child Health: From Technology to Translation. Toronto, Ontario, Canada.
Namdar, K., Wagner, M.W., Sheng, M., Yeom, K.W., Hawkins, C., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. Precision medicine in non-invasive pediatric low-grade glioma molecular subtyping: Outlier identification through multi-class machine learning and bi-institutional MRI analysis (2024) Nature Conference: Precision Child Health: From Technology to Translation. Toronto, Ontario, Canada.
Namdar, K., Wagner, M.W., Sheng, M., Yeom, K.W., Hawkins, C., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. Simulating prospective evaluation of non-invasive pediatric low-grade glioma molecular subtype identification using multi-class incremental learning and bi-institutional MRI (2024) Nature Conference: Precision Child Health: From Technology to Translation. Toronto, Ontario, Canada.
Zakeri, H., Khalvati, F. A Comparative analysis of evidential deep learning framework for uncertainty quantification in 3D MR images (2024) International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, Florida, United States.
Namdar, K., Wagner, M.W., Hawkins, C., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. Radiomics-based molecular subtype identification of pediatric low-grade neuroepithelial tumors using genetic algorithm and CatBoost machine learning pipelines (2024) America Society of Neuroradiology (ASNR). Las Vegas, Nevada, United States.
Namdar, K., Wagner, M.W., Hawkins, C., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. 2D vs 3D MRI-based analysis: a case study for pediatric low-grade neuroepithelial tumor subtype identification (2024) America Society of Neuroradiology (ASNR). Las Vegas, Nevada, United States.
Namdar, K., Wagner, M.W., Hawkins, C., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. Multiclass machine learning models for molecular subtype identification of pediatric low-grade neuroepithelial tumors using bi-institutional MRIs for precision medicine (2024) America Society of Neuroradiology (ASNR). Las Vegas, Nevada, United States.
Stott, S.M., Wu, Y., Namdar, K., Chen, C., Hosseinpour, S., Amirabadi, A., Shroff, M., Khalvati, F.,Doria, A. Correlative assessment of machine learning-based cobb angle measurements and human-based measurements in adolescent idiopathic and congenital scoliosis (2024) The Society for Pediatric Radiology (SPR). Miami, Florida, United States.
Rajapaksa, S., Khan, N., Khalvati, F., Yeh, E.A.Utilizing convolutional neural networks for the identification of pediatric demyelinating disorders through optical coherence tomography imaging (2024) Annual Americas Committee for Treatment and Research in Multiple Sclerosis (ACTRIMS) Forum. West Palm Beach, Florida, United States.
Soldatelli, M.D., Namdar, K., Khalvati, F., Ertl-Wagner, B.B., Wagner, M.W. Multiclass pediatric low-grade glioma molecular subtype identification with ADC Radiomics (2024) American Society for Pediatric Neuroradiology (ASPNR). Sandiego, California, United States.
Ketabi, S., Wagner, M.W., Hawkins, C., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. Radiology report-guided prediction of pediatric low-grade neuroepithelial tumors genetic markers using deep learning. Radiological Society of North America (RSNA). Chicago, Illinois, United States, 2023.
Namdar, K., Wagner, M.W., Ertl-Wagner, B.B., Khalvati, F. Open-Radiomics: A research protocol to make radiomics-based machine learning pipelines reproducible. Radiological Society of North America (RSNA). Chicago, Illinois, United States, 2023.
Namdar, K., Wagner, M.W., Hawkins, C., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. Multiclass radiomics-based models for pediatric low-grade neuroepithelial tumors molecular subtype identification based on open-radiomics protocol. Radiological Society of North America (RSNA). Chicago, Illinois, United States, 2023.
Yoo, J., Wagner, M.W., Tabori, U., Hawkins, C., Ertl-Wagner, B.B., Khalvati, F. A weakly supervised method for brain tumor segmentation in MR images. Radiological Society of North America (RSNA). Chicago, Illinois, United States, 2023.
Namdar, K., Wagner, M.W., Alotaibi, R., Tabori, U., Khalvati, F., Ertl-Wagner, B.B. Multiclass radiomics-based machine learning models for medulloblastoma molecular subtype identification. Radiological Society of North America (RSNA). Chicago, Illinois, United States, 2023.
Namdar, K., Wagner, M.W., Dorigatti Soldatelli, M., Tabori, U., Khalvati, F., Ertl-Wagner, B.B. Multiclass pediatric low-grade neuroepithelial tumor molecular subtype identification with bi-institutional apparent diffusion coefficient MRIs and machine learning. Radiological Society of North America (RSNA). Chicago, Illinois, United States, 2023.
Namdar, K., Wagner, M.W., Hawkins, C., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. Improving pediatric low-grade neuroepithelial tumors molecular subtype identification using a novel AUC loss function for convolutional neural networks, T-CAIREM AI in Medicine Conference. Toronto, Ontario, Canada, 2023.
Yoo, J., Namdar, K., Carey, S., McIntosh, C., Khalvati, F., Rogalla, P. A comprehensive study of radiomics-based machine learning for fibrosis detection. T-CAIREM AI in Medicine Conference. Toronto, Ontario, Canada, 2023.
Kudus, K., Wagner, M.W., Namdar, K., Tabori, U., Ertl-Wagner, B.B., Khalvati, F. Beyond MRI-based hand-crafted features for pretherapeutic molecular status identification of pediatric low-grade neuroepithelial tumors. T-CAIREM AI in Medicine Conference. Toronto, Ontario, Canada, 2023.
Y. Wu, K. Namdar, C. Chen, S. Hosseinpour, M.M. Shroff, A. Doria, F. Khalvati. “Automated Cobb Angle Measurement For Assessment of Adolescence Scoliosis Using Augmented U-Net With Non-Square Kernel.” Annual Meeting of The Society for Pediatric Radiology (SPR), Austin, Texas, USA, 2023.
C. Chen, K. Namdar, Y. Wu, S. Hosseinpour, M.M. Shroff, A. Doria, F. Khalvati. ” Automating Cobb Angle Measurement For Adolescence Idiopathic Scoliosis Using Instance Segmentation.” Annual Meeting of The Society for Pediatric Radiology (SPR), Austin, Texas, USA, 2023.
P. Vafaeikia, M. W. Wagner, C. Hawkins, U. Tabori, B.B. Ertl-Wagner, F. Khalvati. “An End-To-End Radiomics-Based Pediatric Low-Grade Glioma Classification Pipeline.” Annual Meeting and Exhibition of the Radiological Society of North America (RSNA), Chicago, IL, USA, 2022.
K. Namdar, M.W. Wagner, P. Vafaeikia, K. Kudus, F. Khalvati, B.B. Ertl-Wagner. “Pediatric Low-Grade Glioma Molecular Subtype Identification Based on 3D Probability Distributions of Tumor Location In MRI.” Annual Meeting and Exhibition of the Radiological Society of North America (RSNA), Chicago, IL, USA, 2022.
K. Kudus, M. W. Wagner, E. Namdar, K. W. Yeom, B.B. Ertl-Wagner, F. Khalvati. “Increased Confidence of Radiomics Facilitating Pretherapeutic Differentiation of BRAF-altered Pediatric Low-grade Glioma.” Annual Meeting and Exhibition of the Radiological Society of North America (RSNA), Chicago, IL, USA, 2021.
M. W. Wagner, N. Hainc, F. Khalvati, K. Namdar, L. Figueiredo, M. Sheng, S. Laughlin, M. M. Shroff, E. Bouffet, U. Tabori, C. Hawkins, K. W. Yeom, B. B. Ertl-Wagner. “Radiomics of Pediatric Low Grade Gliomas: Toward a Pre-therapeutic Differentiation of BRAF-mutated and BRAF-fused Tumors.” Annual Meeting American Society of Neuroradiology (ASNR), 2021.
D. Deniffel, N. Abraham, K. Namdar, S. Motamed, I. Gujrathi, E. Salinas, F. Khalvati, M. A. Haider. “Individualized Prostate Cancer Risk Assessment using MRI-based Deep Learning Compared to Multivariate Risk Modeling Including PI-RADSv2: A Decision Curve Analysis.” European Congress of Radiology (ECR), Vienna, Austria, 2020.
D. Deniffel, K. Namdar, I. Gujrathi, E. Salinas, A. Toi, A. Finelli, F. Khalvati, M. A. Haider. “Safe Reduction of MRI-targeted Biopsies in Men with PI-RADSv2 Category 3 Lesions: Cross-institutional Validation of a Multivariate Risk Model Based on Clinical Parameters.” European Congress of Radiology (ECR), Vienna, Austria, 2020.
E. Salinas, K. Namdar, F. Khalvati, D. Deniffel, P. Abreu, T. Ivanics, G. Sapisochin, M. A. Haider, P. Veit-Haibach. “Prediction of Tumor Progression and Recurrence in Patients with Hepatocellular Carcinoma Undergoing Trans-arterial Chemoembolization as a Bridge to Transplant Using CT Radiomics Features.” European Congress of Radiology (ECR), Vienna, Austria, 2020.
E. Salinas, F. Khalvati, D. G. O’kane, X. Dong, D. J. Knox, O. Bathe, V. Baracos, S. Gallinger, M. A. Haider. “The Rate of Muscle Loss Measured with CT is a Prognostic Marker in Patients with Unresectable Pancreatic Cancer Receiving FOLFIRINOX.” European Congress of Radiology (ECR), Vienna, Austria, 2020.
X. Liu, F. Khalvati, K. Namdar, S. Fischer, M. A. Haider, K. S. Jhaveri. “Application of Radiomic MRI Features in Differentiation of Combined Hepatocellular Cholangiocarcinoma, Cholangiocarcinoma, and Hepatocellular Carcinoma Using Machine Learning.” Annual Meeting and Exhibition of the Radiological Society of North America (RSNA), Chicago, IL, USA, 2019.
D. Deniffel, Y. Zhang, E. Salinas, R. Satkunasivam, F. Khalvati, M. A. Haider. “Towards Reducing Overutilization of Prostate mpMRI: Using PSA Density to Predict Negative and Indeterminate MRI Scans.” Annual Meeting and Exhibition of the Radiological Society of North America (RSNA), Chicago, IL, USA, 2019.
J. He, E. Tio, L. Wang, C. Dulhanty, F. Khalvati, M. A. Haider, A. Wong. “A Comparative Study Between Apparent Diffusion Imaging and Correlated Diffusion Imaging for Prostate Cancer.” Annual Conference on Vision and Imaging Systems, Waterloo, ON, Canada, 2019. Online Link
L. Wang, C. Dulhanty, A. Chung, F. Khalvati, M. A. Haider, A. Wong. “Zone-DR: Discovery Radiomics via Zone-level Deep Radiomic Sequencer Discovery for Zone-based Prostate Cancer Grading using Diffusion Weighted Imaging.” Annual on Conference on Vision and Imaging Systems, Waterloo, ON, Canada, 2019. Online Link
K. En Low, L. Jimenez-Juan, Y. Ung, J. Zhang, F. Khalvati, A. Oikonomou, M. A. Haider. “CT Texture Features as Predictor of Recurrence in Esophageal Cancer Patients Undergoing Trimodality Treatment.” Royal College of Radiologists Conference (RCR), Liverpool, UK, 2018.
F. Khalvati, E. M. Lobo-Mueller, S. Gallinger, M. A. Haider. “Semi-Automated Radiomic Characterization of Pancreatic Ductal Adenocarcinoma in CT Images.” International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 2018.
F. Khalvati, Y. Zhang, S. Baig, P. Karanicolas, S. Gallinger, M. A. Haider. “A Study of Prognostic Value of CT Radiomic Features in Pancreatic Ductal Adenocarcinoma Across Two Centers.” Annual Meeting and Exhibition of the Radiological Society of North America (RSNA), Chicago, IL, USA, 2017.
R. Al Umairi, F. Mahmood, F. Khalvati, B. Xu, M. Yusishen, L. Jimenez-Juan, M. A. Haider, A. Oikonomou. “Can Radiomics at CT and staging PET/CT Serve as an Imaging Biomarker of EGFR and ALK Alterations in Lung Adenocarcinoma?” World Congress of Thoracic Imaging, Boston, MA, USA, 2017
A. Eilaghi, M. A. Haider, Y. Zhang, A. Oikonomou, L. JimenezJuan, F. Khalvati. “Radiomics Features Provide Reliable Measurements from Manual Contouring of Tumours in Lung Cancer.” European Congress of Radiology (ECR), Vienna, Austria, 2017.
F. Khalvati, J. Zhang, S. Baig, A. Wong, M. A. Haider. “Flipping the Computer Aided Diagnosis (CAD) Training Paradigm for Prostate Cancer: Using PI-RADS Reporting of Multi-Parametric MRI (mpMRI) to Train a CAD System and then Validating with Pathology.” 15th Imaging Network Ontario (ImNO) Symposium, London, ON, Canada, 2017.
A. Karimi, A. G. Chung, M. J. Shafiee, F. Khalvati, M. A. Haider, A. Ghodsi, A. Wong. “Discovery Radiomics via a Mixture of Expert Sequencers using Layered Random Projections (LaRP) for Prostate Cancer Classification.” 15th Imaging Network Ontario (ImNO) Symposium, London, ON, Canada, 2017.
D. Kumar, V. Menkovski, F. Khalvati, M. A. Haider, A. Wong. “Deep Medical Imaging Visualization for Clinical Decision Support.” 15th Imaging Network Ontario (ImNO) Symposium, London, ON, Canada, 2017.
A. Eilaghi, F. Khalvati, S. Baig, S. Gallinger, P. Karanicolas, M. A. Haider. “CT Texture Parameters are Promising Prognostic Biomarkers in Pancreatic Ductal Adenocarcinoma.” Annual Meeting and Exhibition of the Radiological Society of North America (RSNA), Chicago, IL, USA, 2016.
A. Oikonomou, F. Khalvati, U. Tarique, L. Jimenez-Juan, G. Tomlinson, C. Caldwell, M. A. Haider, P. Cheung. “Texture Analysis of Early Stage Lung Cancer on Positron Emission Tomography/Computed Tomography (PET/CT) as a Predictor of Clinical Outcome post Stereotactic Body Radiotherapy (SBRT).” Annual Meeting of Society of Thoracic Radiology, Scottsdale, AZ, USA, 2016.
Y. Zhang, A. Eilaghi, A. Wong, A. Oikonomou, M. A. Haider, F. Khalvati. “Radiomics Feature Clusters and Prognostic Signatures Specific for Lung Cancer.” 14th Imaging Network Ontario (ImNO) Symposium, Toronto, ON, Canada, 2016.
J. Zhang, A. Eilaghi, M. A. Haider, A. Wong, F. Khalvati. “Optimized Correlated Diffusion Imaging for Prostate Cancer Detection.” 14th Imaging Network Ontario (ImNO) Symposium, Toronto, ON, Canada, 2016.
A. Eilaghi, S. Baig, J. Zhang, A. Wong, P. Karanicolas, S. Gallinger, F. Khalvati, M. A. Haider. “Radiomics Features Analysis for Tumor Characterization in Pancreatic Ductal Adenocarcinoma.” 14th Imaging Network Ontario (ImNO) Symposium, Toronto, ON, Canada, 2016. Won Magnum Cum Laude Paper Award.
A. Boroomand, E. Li, M. J. Shafiee, F. Khalvati, M. A. Haider, A. Wong. “A Unified Reconstruction Framework for Compensated Magnetic Resonance Imaging”, 14th Imaging Network Ontario (ImNO) Symposium, Toronto, ON, Canada, 2016.
A. G. Chung, M. J. Shafiee, D. Kumar, F. Khalvati, M. A. Haider, A. Wong. “Discovery Radiomics via Layered Random Projection (LaRP) Sequencers for Prostate Cancer Classification.” 14th Imaging Network Ontario (ImNO) Symposium, Toronto, ON, Canada, 2016. Won Magnum Cum Laude Paper Award.
D. Kumar, M. J. Shafiee, A. G. Chung, F. Khalvati, M. A. Haider, A. Wong. “Discovery Radiomics for Lung Cancer Classification.” 14th Imaging Network Ontario (ImNO) Symposium, Toronto, ON, Canada, 2016.
S. A. Haider, A. G. Chung, M. J. Shafiee, H. Grewal, F. Khalvati, A. Oikonomou, M. A. Haider, A. Wong. “Single-Click Lung Nodule Contouring Method Using a Hierarchical Conditional Random Field (HCRF).” 14th Imaging Network Ontario (ImNO) Symposium, Toronto, ON, Canada, 2016.
M. A. Haider, A. Vosough, F. Khalvati, A. Kiss, B. Ganeshan, G. Bjarnason. “Prediction of Survival in Patients with Metastatic Clear Cell Carcinoma Treated with Targeted Anti-angiogenic Agent Sunitinib via CT Texture Analysis.” Annual Meeting and Exhibition of the Radiological Society of North America (RSNA), Chicago, IL, USA, 2015.
F. Khalvati, A. Wong, M. A. Haider. “A Radiomics-based Approach for Prostate Cancer Detection via Incorporating Interpatient Variation in ADC Map.” Canadian Cancer Research Conference. Montreal, Quebec, Canada, 2015.
J. Zhang, Khalvati, A. Wong, M. A. Haider. “Superpixel-based Prostate Cancer Detection from Diffusion Magnetic Resonance Imaging.” Annual Conference on Vision and Imaging Systems, Waterloo, ON, Canada, 2015.
E. Li, M. J. Shafiee, A. Boroomand, F. Khalvati, M. A. Haider, A. Wong. “Compensated Diffusion Magnetic Resonance Imaging.” Annual Conference on Vision and Imaging Systems, Waterloo, ON, Canada, 2015.
A. Wong, A. G. Chung, D. Kumar, M. J. Shafiee, F. Khalvati, M. A. Haider. “Discovery Radiomics for Imaging-driven Quantitative Personalized Cancer Decision Support.” Annual Conference on Vision and Imaging Systems, Waterloo, ON, Canada, 2015.
C. Scharfenberger, D. Lui, F. Khalvati, A. Wong, M. A. Haider. “Semi-Automatic Prostate Segmentation via a Hidden Markov Model with Anatomical and Textural Priors.” Annual Meeting and Exhibition of International Society for Magnetic Resonance in Medicine (ISMRM), Toronto, ON, Canada, 2015.
E. Li, M. J. Shafiee, A. G. Chung, F. Khalvati, A. Wong, M. A. Haider. “Enhanced Reconstruction of Compressive Sensing MRI via Cross-Domain Stochastically Fully-Connected Random Field Model.” Annual Meeting and Exhibition International Society for Magnetic Resonance in Medicine (ISMRM), Toronto, ON, Canada, 2015.
A. Boroomand, M. J. Shafiee, A. Wong, F. Khalvati, P. Fieguth, M. A. Haider. “Noise-Compensated Bias Correction of MRI via a Stochastically Fully-Connected Conditional Random Field Model.” Annual Meeting and Exhibition of International Society for Magnetic Resonance in Medicine (ISMRM), Toronto, ON, Canada, 2015.
D. S. Cho, F. Khalvati, A. Wong, D. A. Clausi, M. A. Haider. “Prostate DWI Co-Registration via Maximization of Hybrid Statistical Likelihood and Cross-Correlation for Improved ADC and Computed Ultra-High b-value DWI Calculation.” Annual Meeting and Exhibition of International Society for Magnetic Resonance in Medicine (ISMRM), Toronto, ON, Canada, 2015.
A. Oikonomou, U. Tarique, L. Jimenez-Juan, F. Khalvati, L. Ehrlich, G. Tomlinson, M. A. Haider, P. Cheung. “Tumor Heterogeneity and Intensity of Early Stage Lung Cancer on PET and Computed Tomography as a Predictor of Response to Stereotactic Body Radiotherapy (SBRT).” Annual Meeting of Society of Thoracic Radiology, Carlsbad, CA, USA, 2015.
F. Khalvati, A. Wong, G. A. Bjarnason, M. A. Haider. “Semi-Automatic Normalized Entropy Characterization of Metastatic Renal Cell Cancer via Spatio-Textural Tumour Classification.” International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Chicago, IL, USA, 2014.
S. Martin, N. Patil, S. Gaede, A. Louie, G. Bauman, D. D’Souza, T. Sexton, D. Palma, F. Khalvati, G. Rodrigues. “A Multi-phase Technological Validation of a MRI Prostate Cancer Computer Autosegmentation Software Algorithm.” Proceedings of American Society for Therapeutic Radiology and Oncology (ASTRO) Annual meeting, Miami Beach, FL, USA, 2011.

Patents

H. R. Tizhoosh, F. Khalvati. “Method and System for Binary and Quasi-Binary Atlas-Based Auto-Contouring of Volume Sets in Medical Images.” Patent No: US 9,361,701 B2, (Granted), 2016. Online Link
F. Khalvati, H. R. Tizhoosh. “System, Method and Computer Program for Automated Window Memoization.” Patent No: US 9,076,240 B2, 2015 (Granted). Online Link
H. R. Tizhoosh, F. Khalvati. “Computer System and Method for Atlas-Based Consensual and Consistent Contouring of Medical Images.” International Publication Number: WO 2013/040693 A1, 2013 (Filed). Online Link

Thesis

F. Khalvati. “Computational Redundancy in Image Processing.” Ph.D. Thesis, University of Waterloo, Waterloo, ON, Canada, 2008. Online Link
F. Khalvati. “Modeling and Analysis of Amorphous Si/Crystalline Si Heterojunction Photovoltaic Cells.” M.A.Sc. Thesis, University of Waterloo, Waterloo, ON, Canada, 2003. Online Link