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Books and Book Chapters

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

Journal Papers (Published)

1. Y. Wu, K. Namdar, C. Chen, S. Hosseinpour, M.M. Shroff, A. Doria, F. Khalvati. “Automated adolescence scoliosis detection using Augmented U-Net with non-square kernels.” Canadian Association of Radiologists Journal, 2023. Online Link
2. M. Taheri-Shirazi, K. Namdar, K. Ling, K. Karmali, M.D. McCradden, W. Lee, F. Khalvati. “Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning.” Frontiers in Public Health, section Digital Public Health, 2023. Online Link
3. S. Rajapaksa, F. Khalvati. “Relevance Maps: A Weakly-Supervised Segmentation Method for 3D Brain Tumours in MRIs.” Frontiers in Radiology, section Artificial Intelligence in radiology, 2022. Online Link
4. H. Jin, M.W. Wagner, B.B. Ertl-Wagner, F. Khalvati. “An Educational Graphical User Interface To Construct Convolutional Neural Networks For Teaching Artificial Intelligence In Radiology.” Canadian Association of Radiologists Journal, 2022. Online Link
5. A. Watanabe, S. Ketabi, K. Namdar, F. 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
6. Y.T. Leung, F. Khalvati. “Exploring Covid-19 Related Stressors Using Topic Modeling.” Journal Of Medical Internet Research, 2022. Online Link
7. M.W. Wagner, K. Namdar, M. Napoleone, N. Hainc, A. Amirabadi, A. Fonseca, S. Laughlin, M.M. Shroff, E. Bouffet, C. Hawkins, F. Khalvati, U. Bartels, B.B. Ertl-Wagner. “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
8. M.W. Wagner, K. Namdar, A. Alqabbani, N. Hainc, L. Nobre Figuereido, M. Sheng, M. M. Shroff, E. Bouffet, U. Tabori, C. Hawkins, M. Zhang, K. W. Yeom, F. Khalvati, B.B. 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
9. S. Monah, M.W. Wagner, A. Biwas, F. Khalvati, L. Erdman, A. Amirabadi, L. Vidarsson, M. McCradden, B.B. Ertl-Wagner. “Data Governance Functions to Support Responsible Data Stewardship in Pediatric Radiology Research Studies using Artificial Intelligence.” Pediatric Radiology, 2022. Online Link
10. S. Motamed, P. Rogalla, F. Khalvati. “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
11. B.B. Ertl-Wagner, F. Khalvati. “The Data Behind the Image – Deep-Learning and its Potential Impact in Neuro-Oncological Imaging.” Neuro-Oncology, 2021. Online Link
12. M.W. Wagner*, K. Namdar*, A. Biswas, S. Monah, F. Khalvati#, B.B. Ertl-Wagner#. “Radiomics, Machine Learning, Artificial Intelligence – What the Neuroradiologist Needs to Know.” Neuroradiology, 2021. (*, #: equal contribution) Online Link
13. R. Hao, K. Namdar, L. Liu, F. Khalvati. “A Transfer Learning Based Active Learning Framework for Brain Tumor Classification.” Frontiers in Artificial Intelligence, 2021. Online Link
14. S. Motamed, P. Rogalla, F. Khalvati. “RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray.” Nature Scientific Reports, 11, 8602, 2021. Online Link 
15. K. Namdar, M. A. Haider, F. Khalvati. “A Modified AUC For Training Convolutional Neural Networks: Taking Confidence Into Account.” Frontiers in Artificial Intelligence, 2021. Online Link
16. R. Hao, K. Namdar, L. Liu, M. A. Haider, F. Khalvati. “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
17. Y. Zhang, E. M. Lobo-Mueller, P. Karanicolas, S. Gallinger, M. A. Haider, F. Khalvati. “Improving Prognostic Performance in Resectable Pancreatic Ductal Adenocarcinoma using Radiomics and Deep Learning Features Fusion in CT Images.” Nature Scientific Reports, 11, 1378, 2021. Online Link
18. 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 Pretherapeutic Differentiation of BRAF- Mutated and BRAF-Fused Tumors.” American Journal of Neuroradiology, 11;42(4):759-765, 2021. Online Link
19. X. Liu, F. Khalvati, K. Namdar, S. Fischer, S. Lewis, B. Taouli, M. A. Haider, K. S. Jhaveri. “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
20. 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 (2021). European Radiology. Online Link
21. T. Ivanics, T. E. Salinas- Miranda, P. Abreu, F. Khalvati, K. Namdar, X. Dong, D. Deniffel, A. Gorgen, L. Erdman, K. Jhaveri, M. A. Haider, P. Veit-Haibach, G. Sapisochin. “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
22. Y. Zhang, E. M. Lobo-Mueller, P. Karanicolas, S. Gallinger, M. A. Haider, F. Khalvati. “Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma.” Frontiers in Artificial Intelligence – Medicine and Public Health, 2020. Online Link
23. Y. Zhang, E. M. Lobo-Mueller, P. Karanicolas, S. Gallinger, M. A. Haider, F. Khalvati. “CNN-based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging.” BMC Medical Imaging, 20(11), 2020. Online Link
24. E. Salinas-Miranda, F. Khalvati, K. Namdar, D. Deniffel X. Dong, E. Abbas, J. M. Wilson, G. M. O’Kane, J. Knox, S. Gallinger, M. A. Haider. “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
25. C. Dulhanty, L. Wang, M. Cheng, H. Gunraj, F. Khalvati, M. A. Haider, A. Wong. “Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-level Prostate Cancer Sensing.” Sensors, 2020. Online Link
26. D. Deniffel, Y. Zhang, E. Salinas, R. Satkunasivam, F. Khalvati, M. A. Haider. “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  
27. D. Deniffel, N. Abraham, K. Namdar, X. Dong, E. Salinas, L. Milot, F. Khalvati, M. A. Haider. “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
28. S. Yoo, I. Gujrathi, M. A. Haider, F. Khalvati. “Prostate Cancer Detection via Deep Convolutional Neural Networks.” Nature Scientific Reports, 9, 19518, 2019. Online Link
29. F. Khalvati, Y. Zhang, S. Baig, E. M. Lobo Mueller, P. Karanicolas, S. Gallinger, M. A. Haider. “Prognostic Value of CT Radiomic Features in Pancreatic Ductal Adenocarcinoma.” Nature Scientific Reports, 9, 5449, 2019. Online Link
30. M. Zhang, L. Milot, F. Khalvati, L. Sugar, M. Downes, S. Baig, L. Klotz, M. A. Haider. “Value of Increasing Biopsy Cores per Target with Cognitive MRI-targeted Transrectal US Prostate Biopsy.” Radiology, Vol. 291, No. 1, 2019. Online Link
31. F. Khalvati, J. Zhang, A. G. Chung, M. J. Shafiee, A. Wong, M. A. Haider. “MPCaD: A Multi-Scale Radiomics-Driven Framework for Automated Prostate Cancer Localization and Detection.” BMC Medical Imaging, 18:16, 2018. Online Link
32. A. Oikonomou, F. Khalvati, P. N. Tyrrell, M. A. Haider, U. Tarique, L. Jimenez-Juan, M. C. Tjong, I. Poon, A. Eilaghi, L. Ehrlich, P. Cheung. “Radiomics Analysis at PET/CT Contributes to Prognosis of Recurrence and Survival in Lung Cancer Treated with Stereotactic Body Radiotherapy.” Nature Scientific Reports, 8, 4003, 2018. Online Link
33. Y. Zhang, A. Oikonomou, A. Wong, M. A. Haider, F. Khalvati. “Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer.” Nature Scientific Reports, 7, 46349, 2017. Selected Among Editor’s Choice in Sep 2019. Online Link
34. T. Clark, J. Zhang, S. Baig, A. Wong, M. A. Haider, F. Khalvati. “Fully Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion-weighted MRI using Convolutional Networks.” Journal of Medical Imaging, Vol. 4, No. 4:041307, 2017. Online Link 
35. M. J. Shafiee, A. G. Chung, F. Khalvati, M. A. Haider, A. Wong. “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 
36. A. Eilaghi, S. Baig, Y. Zhang, J. Zhang, P. Karanicolas, S. Gallinger, F. Khalvati, M. A. Haider. “CT Texture Features are Associated with Overall Survival in Pancreatic Ductal Adenocarcinoma.” BMC Medical Imaging, 2017. Online Link 
37. M. A. Haider, A. Vosough, F. Khalvati, A. Kiss, B. Ganeshan, G. Bjarnason. “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
38. F. Khalvati, A. Salmanpour, S. Rahnamayan, M. A. Haider, H. R. Tizhoosh. “Sequential Registration-Based Segmentation of the Prostate Gland in MR Image Volumes.” Journal of Digital Imaging, Vol. 29, pp. 254-263, 2016. Online Link 
39. A. Cameron, F. Khalvati, M. A. Haider, A. Wong. “MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection.” IEEE Transactions on Biomedical Engineering, Vol. 63, No. 6, pp. 1145-1156, 2016. Online Link 
40. E. Li, F. Khalvati, M. J. Shafiee, M. A. Haider, A. Wong. “Sparse Reconstruction of Compressive Sensing MRI Using Cross-Domain Stochastically Fully Connected Conditional Random Field.” BMC Medical Imaging, 2016. Online Link 
41. A. Boroomand, M. J. Shafiee, F. Khalvati, M. A. Haider, A. Wong. “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 
42. F. Khalvati, C. Gallego Ortiz, S. Balasingham, A. L. Martel. “Automated Segmentation of Breast in 3D MR Images Using a Robust Atlas.” IEEE Transactions on Medical Imaging, Vol. 34, No. 1, pp. 116-125, 2015. Online Link 
43. F. Khalvati, A. Wong, M. A. Haider. “Automated Prostate Cancer Detection via Comprehensive Multi-Parametric Magnetic Resonance Imaging Texture Feature Models.” BMC Medical Imaging, pp. 15-27, 2015. Online Link
44. F. Khalvati, M. D. Aagaard, H. R. Tizhoosh. “Window Memoization: Toward High-Performance Image Processing Software.” Journal of Real-Time Image Processing, Vol. 10, No. 1, pp. 5-25, 2015. Online Link
45. A. G. Chung, F. Khalvati, M. J. Shafiee, M. A. Haider, A. Wong. “Prostate Cancer Detection via a Quantitative Radiomics-Driven Conditional Random Field Framework.” IEEE Access, Vol. 3, pp. 2531-2541, 2015. Online Link
46. A. Othman, H. R. Tizhoosh, F. Khalvati. “EFIS-Evolving Fuzzy Image Segmentation.” IEEE Transactions on Fuzzy Systems, Vol. 22, No. 1, pp. 72-82, 2014. Online Link
47. F. Khalvati, A. Salmanpour, S. Rahnamayan, G. Rodrigues, H. R. Tizhoosh. “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 
48. S. Martin, G. Rodrigues, N. Patil, G. Bauman, D. D’Souza, T. Sexton, D. Palma, A. V. Louie, F. Khalvati, H. R. Tizhoosh, S. Gaede. “A Multi-Phase Validation of Atlas-Based Automatic and Semi-Automatic Segmentation Strategies for Prostate MRI.” International Journal of Radiation Oncology, Biology, Physics. Vol. 85, No. 1, pp. 95-100, 2013. Online Link 
49. F. Khalvati, M. D. Aagaard. “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 
50. M. F. Baroughi, R. Jayakumar, Y. Vygranenko, F. Khalvati, S. Sivoththaman. “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 

Conference Full Length Papers (Published)

1. S. Rajapaksa, F. Khalvati. “Optimized Global Perturbation Attacks For Brain Tumour ROI Extraction From Binary Classification Models.” International Conference on Neural Information Systems (NeurIPS) Workshops, 2022. Online Link
2. P. Vafaeikia, M.W. Wagner, U. Tabori, C. Hawkins, B.B. Ertl-Wagner, F. Khalvati. “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
3. S. Rajapaksa, F. Khalvati. “Localized Perturbations for Semi-supervised Segmentation of Glioma Brain Tumours. International Conference on Neural Information Systems (NeurIPS) Workshops.” 2021. Online Link
4. S. Motamed, F. Khalvati. “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.
5. S. Motamed, F. Khalvati. “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.
6. S. Motamed, R. Rogalla, F. Khalvati. “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
7. S. Akbarian, L. Seyyed-Kalantari, F. Khalvati, E. Dolatabadi. “Attention Transfer Outperforms Transfer Learning in Medical Image Disease Classifiers.” International Conference on Neural Information Systems (NeurIPS) Workshops, 2020.
8. K. Namdar, I. Gujrathi, M. A. Haider, F. Khalvati. “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
9. F. Khalvati, Y. Zhang, P. H. U. Le, I. Gujrathi, M. A. Haider. “PI-RADS Guided Discovery Radiomics for Characterization of Prostate Lesions with Diffusion-Weighted MRI.” SPIE Medical Imaging, San Diego, CA, USA, 2019. Online Link
10. X. Hu, A. G. Chung, P. Fieguth, F. Khalvati, M. A. Haider, 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
11. F. Khalvati, J. Zhang, A. Wong, M. A. Haider. “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 
12. T. Clark, A. Wong, M. A. Haider, F. Khalvati. “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 
13. D. S. Cho, F. Khalvati, D. Clausi, A. Wong. “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
14. J. Zhang, S. Baig, A. Wong, M. A. Haider, F. Khalvati. “Segmentation of Prostate in Diffusion MR Images via Clustering.” 14th International Conference on Image Analysis and Recognition (ICIAR), Montreal, QC, Canada, 2017. Online Link 
15. D. Kumar, A. G. Chung, M. J. Shafiee, F. Khalvati, M. A. Haider, A. Wong. “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 
16. A. Karimi, A. G. Chung, M. J. Shafiee, F. Khalvati, M. A. Haider, A. Ghodsi, A. Wong. “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 
17. S. Yoo, M. A. Haider, F. Khalvati. “Estimating Optimal Depth of VGG Net with Tree-Structured Parzen Estimators.” Annual Conference on Vision and Intelligent Systems, Waterloo, ON, Canada, 2017. Online Link 
18. F. Khalvati, J. Zhang, S. Baig, M. A. Haider, A. Wong. “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
19. J. Zhang, S. Baig, A. Wong, M. A. Haider, F. Khalvati. “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 
20. F. Khalvati, J. Zhang, M. A. Haider, A. Wong. “Enhanced Dual-Stage Correlated Diffusion Imaging.” International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 2016. Online Link 
21. A. Boroomand, E. Li, M. J. Shafiee, M. A. Haider, F. Khalvati, A. Wong. “A Unified Bayesian-based Compensated Magnetic Resonance Imaging.” International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 2016. Online Link 
22. A. Othman, H. R. Tizhoosh, F. Khalvati. “Self-Configuring and Evolving Fuzzy Image Thresholding.” IEEE International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 2015. Online Link
23. M. J. Shafiee, A. G. Chung, D. Kumar, A. Wong, F. Khalvati, M. A. Haider. “Discovery Radiomics via StochasticNet Sequencers for Cancer Detection.” International Conference on Neural Information Systems (NIPS) Workshops, Montreal, QC, Canada, 2015. Online Link 
24. Z. Camlica, H. R. Tizhoosh, F. Khalvati. “Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data.” IEEE International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 2015. Online Link 
25. Z. Camlica, H. R. Tizhoosh, F. Khalvati. “Autoencoding the Retrieval Relevance of Medical Images.” IEEE International Conference on Image Processing, Tools, Theory and Applications (IPTA), Orleans, France, 2015. Online Link 
26. A. Wong, F. Khalvati, M. A. Haider. “Dual-Stage Correlated Diffusion Imaging.” IEEE International Symposium on Biomedical Imaging (ISBI), Brooklyn, NY, USA, 2015. Online Link 
27. A. G. Chung, C. Scharfenberger, F. Khalvati, A. Wong, M. A. Haider. “Statistical Textural Distinctiveness in Multi-Parametric Prostate MRI for Suspicious Region Detection.” International Conference on Image Analysis and Recognition (ICIAR), Niagara Falls, ON, Canada, 2015. Online Link 
28. S. A. Haider, M. J. Shafiee, A. G. Chung, F. Khalvati, A. Oikonomou, A. Wong, M. A. Haider. “Single-Click, Semi-Automatic Lung Nodule Contouring Using Hierarchical Conditional Random Fields.” IEEE International Symposium on Biomedical Imaging (ISBI), Brooklyn, NY, USA, 2015. Online Link 
29. F. Khalvati, A. Modhafar, A. Cameron, A. Wong, M. A. Haider. “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, Boston, MA, USA, 2014. Online Link 
30. A. Cameron, A. Modhafar, F. Khalvati, D. Lui, M. J. Shafiee, A. Wong, M. A. Haider. “Multi-Parametric MRI Prostate Cancer Analysis via a Hybrid Morphological-Textural Model.” International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Chicago, IL, USA, 2014. Online Link
31.  F. Khalvati, A. L. Martel. “Atlas-Based Segmentation of Breast MR Images.” International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshops, Nagoya, Japan, 2013. Online Link 
32. F. Khalvati, H. R. Tizhoosh. “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), Las Vegas, NV, USA, 2011. Online Link 
33. F. Khalvati, M. Kianpour, H. R. Tizhoosh. “Cascaded Window Memoization for Medical Imaging.” International Conference on Artificial Intelligence Applications and Innovations (AIAI), Corfu, Greece, 2011. Online Link 
34. F. Khalvati, H. R. Tizhoosh, A. R. Hajian. “Increasing Computational Redundancy of Digital Images via Multiresolutional Matching.” International Conference on Image Analysis and Recognition (ICIAR), Halifax, NS, Canada, 2009. Online Link 
35. F. Khalvati, H. R. Tizhoosh. “An Efficient Architecture for Hardware Implementations of Image Processing Algorithms.” IEEE Symposium on Computational Intelligence for Image Processing (CIIP), Nashville, TN, USA, 2009. Online Link 
36. F. Khalvati, M. D. Aagaard, H. R. Tizhoosh. “Accelerating Image Processing Algorithms Based on the Reuse of Spatial Patterns.” Canadian Conference on Electrical and Computer Engineering (CCECE), Vancouver, BC, Canada, 2007. Online Link 
37. F. Khalvati, H. R. Tizhoosh, M. D. Aagaard. “Opposition-Based Window Memoization for Morphological Algorithms.” IEEE Symposium on Computational Intelligence in Image and Signal Processing (CIISP), Honolulu, HI, USA, 2007. Won IEEE Outstanding Student-Paper Travel Award. Online Link 
38. M. D. Aagaard, V. C. Ciubotariu, J. T. Higgins, F. Khalvati. “Combining Equivalence Verification and Completion Functions.” Formal Methods in Computer-Aided Design (FMCAD), Austin, TX, USA, 2004. Online Link 
39. F. Khalvati, S. Sivoththaman. “Quantum Efficiency Modeling of Amorphous /Crystalline Silicon Heterojunction Photovoltaic Devices.” Material Research Society Symposium (MRS), Boston, MA, USA, 2003. 

arXiv Manuscripts

1. S. Motamed, F. Khalvati. “Vanishing Twin GAN: How Training a Weak Generative Adversarial Network Can Improve Semi-supervised Image Classification.” arXiv, 2021. Online Link 
2. Akbarian, L. Seyyed-Kalantari, F. Khalvati, E. Dolatabadi. “Evaluating Knowledge Transfer in Neural Network for Medical Images.” arXiv, 2020. Online Link
3. P. Vafaeikia, K. Namdar, F. Khalvati. “A Brief Review of Deep Multi-task Learning and Auxiliary Task Learning.” arXiv, 2020. Online Link
4. 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.” arXiv, 2019. Online Link

Conference 1-Page Abstracts

1. 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.
2. 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.
3. 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. 
4. 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. 
5. 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. 
6.  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. 
7. 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. 
8. 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.
9. 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. 
10. 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. 
11. 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. 
12. 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. 
13. 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
14. 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
15. 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. 
16. 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. 
17. 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.
18. 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
19. 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. 
20. 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. 
21. 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. 
22. 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. 
23. 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. 
24. 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. 
25. 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. 
26. 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. 
27. 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. 
28. 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. 
29. 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. 
30. 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. 
31. 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. 
32. 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.
33. 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.
34. 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. 
35. 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. 
36. 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. 
37. 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.
38. 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. 
39. 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. 
40. 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. 
41. 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.
42. 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.
43. 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

1. 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
2. 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  
3. 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

1. F. Khalvati. “Computational Redundancy in Image Processing.” Ph.D. Thesis, University of Waterloo, Waterloo, ON, Canada, 2008. Online Link
2. 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