1. Jeremy Kawahara. Diagnosing Skin Lesions from Images with Convolutional Neural Networks. Doctoral Thesis, School of Computing Science, Faculty of Applied Sciences, Simon Fraser University, May 2019. [bibtex-key = phd2019kawahara]



2. Jeremy Kawahara. Uncertainty in Probabilistic Image Registration. Master's Thesis, School of Computing Science, Faculty of Applied Sciences, Simon Fraser University, August 2013. [bibtex-key = msc2013kawahara]

1. Ashish Sinha, Jeremy Kawahara, Arezou Pakzad, Kumar Abhishek, Matthieu Ruthven, Enjie Ghorbel, Anis Kacem, Djamila Aouada, and Ghassan Hamarneh. DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images (Sinha, Kawahara, Pakzad: Joint first authors). Medical Image Analysis (MedIA), 000(103145):000, 2024. Keyword(s): Dermatology, Machine Learning, Deep Learning, Segmentation, Synthesis/Simulation/Augmentation, Datasets. [bibtex-key = media2024]



2. Mengliu Zhao, Jeremy Kawahara, Kumar Abhishek, Sajjad Shamanian, and Ghassan Hamarneh. Skin3D: Detection and Longitudinal Tracking of Pigmented Skin Lesions in 3D Total-Body Textured Meshes (Zhao and Kawahara: Joint first authors). Medical Image Analysis (MedIA), 77(102329):1-10, 2022. Keyword(s): Dermatology, Machine Learning, Deep Learning, Optimization, Registration and Matching, Graph based, Tracking, Datasets. [bibtex-key = media2022]



3. Kumar Abhishek, Jeremy Kawahara, and Ghassan Hamarneh. Predicting the Clinical Management of Skin Lesions using Deep Learning. Nature - Scientific Reports, 11(7769):1-14, 2021. Keyword(s): Dermatology, Machine Learning, Deep Learning, Clinical Management. [bibtex-key = nature_sr2021b]



4. Jeremy Kawahara, Sara Daneshvar, Giuseppe Argenziano, and Ghassan Hamarneh. Seven-Point Checklist and Skin Lesion Classification using Multi-Task Multi-Modal Neural Net. IEEE Journal of Biomedical and Health Informatics (IEEE JBHI) (Special Issue on Skin Lesion Image Analysis for Melanoma Detection), 23(2):538-546, 2019. Keyword(s): Color/Multichannel/Vector-valued, Deep Learning, Dermatology, Classification, Multimodal, Datasets. [bibtex-key = jbhi2019a]



5. Jeremy Kawahara and Ghassan Hamarneh. Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features. IEEE Journal of Biomedical and Health Informatics (IEEE JBHI) (Special Issue on Skin Lesion Image Analysis for Melanoma Detection), 23(2):578-585, 2019. Keyword(s): Color/Multichannel/Vector-valued, Deep Learning, Dermatology, Classification, Segmentation. [bibtex-key = jbhi2019b]



6. Jeremy Kawahara, Colin J. Brown, Steven Miller, Brian G. Booth, Vann Chau, Ruth Grunau, Jill Zwicker, and Ghassan Hamarneh. BrainNetCNN: Convolutional Neural Networks for Brain Networks; Towards Predicting Neurodevelopment (Brown and Kawahara: Joint first authors). NeuroImage, 146(1):1038-1049, 2017. Keyword(s): Diffusion MRI/Tensor-valued, Neurodevelopment, Connectome, Machine Learning, Deep Learning. [bibtex-key = neuroimage2017]



7. Saeid Asgari Taghanaki, Jeremy Kawahara, Brandon Miles, and Ghassan Hamarneh. Pareto-Optimal Multi-objective Dimensionality Reduction Deep Auto-Encoder for Mammography Classification. Computer Methods and Programs in Biomedicine, 145:85-93, 2017. Keyword(s): Machine Learning, Deep Learning, Optimization. [bibtex-key = cmpb2017]

1. Kumar Abhishek, Jeremy Kawahara, and Ghassan Hamarneh. Segmentation Style Discovery: Application to Skin Lesion Images. In Medical Image Computing and Computer-Assisted Intervention (MICCAI) ISIC Skin Image Analysis Workshop (MICCAI ISIC), pages 000–000, 2024. Keyword(s): Dermatology, Color/Multichannel/Vector-valued, Machine Learning, Deep Learning, Segmentation. [bibtex-key = miccai_isic2024a]



2. Kumar Abhishek, Jeremy Kawahara, and Ghassan Hamarneh. Direct AI-based Prediction of Clinical Management Bypassing Diagnosis: Application to Skin Lesions. In UBC Dermatology and Skin Science: Skin Research Day, 2021. Keyword(s): Deep Learning, Dermatology. [bibtex-key = skin2021b]



3. Mia McLean, Lynne Williams, Jeremy Kawahara, Colin J. Brown, Ghassan Hamarneh, Joanne Weinberg, Anne Synnes, Steven Miller, and Ruth Grunau. Associations Between Neonatal Pain-Related Stress, Neonatal Brain Structural Connectome, and Behavior at School Age in Children Born Very Preterm. In 6th Annual Research Day Brain, Behaviour and Development (BB&D), Vancouver, Canada, pages 1, 2021. Keyword(s): Diffusion MRI/Tensor-valued, Neurodevelopment, Connectome, Machine Learning. [bibtex-key = bcbbd2021]



4. Mengliu Zhao, Jeremy Kawahara, Sajjad Shamanian, Kumar Abhishek, and Ghassan Hamarneh. Computerized Localization and Tracking of Pigmented Skin Lesions on 3D Whole Body Textured Skin Meshes (Zhao and Kawahara: Joint first authors). In UBC Dermatology and Skin Science: Skin Research Day, 2021. Keyword(s): Deep Learning, Tracking, Dermatology. [bibtex-key = skin2021a]



5. Mengliu Zhao, Sajjad Shamanian, Priyanka Chandrashekar, Kumar Abhishek, Jeremy Kawahara, and Ghassan Hamarneh. Acquisition and Analysis of 3D Whole Body Skin Images for Dermatological Studies. In The 2nd Annual Tri-Cluster Research Day, Canada, pages 1, 2020. Keyword(s): Deep Learning, Dermatology. [bibtex-key = tricluster2020a]



6. Mengliu Zhao, Sajjad Shamanian, Priyanka Chandrashekar, Kumar Abhishek, Jeremy Kawahara, and Ghassan Hamarneh. Skin Lesion Localization and Tracking on 3D Whole Body Colored Surface Images. In Centre for Artificial Intelligence Decision-making and Action (CAIDA), BC's AI Showcase, Canada, pages 1, 2020. Keyword(s): Deep Learning, Dermatology. [bibtex-key = caida2020a]



7. Yiqi Yan, Jeremy Kawahara, and Ghassan Hamarneh. Melanoma Recognition via Visual Attention. In Information Processing in Medical Imaging (IPMI), volume 11492, pages 793–804, 2019. Keyword(s): Machine Learning, Deep Learning, Segmentation, Dermatology. [bibtex-key = ipmi2019]



8. Saeed Izadi, Zahra Mirikharaji, Jeremy Kawahara, and Ghassan Hamarneh. Generative Adversarial Networks to Segment Skin Lesions. In IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pages 881-884, 2018. Keyword(s): Machine Learning, Dermatology, Color/Multichannel/Vector-valued, Deep Learning, Generative Adversarial Network, Segmentation. [bibtex-key = isbi2018b]



9. Jeremy Kawahara, Colin J. Brown, and Ghassan Hamarneh. Connectome Priors in Deep Neural Networks to Predict Autism (Kawahara and Brown: Joint first authors). In IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pages 110-113, 2018. Keyword(s): Diffusion MRI/Tensor-valued, Autism, Connectome, Machine Learning, Deep Learning, Classification. [bibtex-key = isbi2018c]



10. Zahra Mirikharaji, Saeed Izadi, Jeremy Kawahara, and Ghassan Hamarneh. Deep Auto-context Fully Convolutional Neural Network for Skin Lesion Segmentation. In IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pages 877-880, 2018. Keyword(s): Machine Learning, Dermatology, Color/Multichannel/Vector-valued, Deep Learning, Segmentation. [bibtex-key = isbi2018a]



11. Adria Romero-Lopez, Tory Jarmain, Latif Abid, Jevin Maltais, Albert Jimenez, Jeremy Kawahara, Ghassan Hamarneh, Shuang Ao, Mohamed Akrout, Anirudh Challa, Michael Aristocrat, Katrina Cirone, Stephen Solis, and Andrew McCann. Q&A-informed image-based deep learning for skin disease recognition in the real world. In Self-Organizing Conference on Machine Learning (SOCML), pages 1, 2018. Keyword(s): Machine Learning, Dermatology, Color/Multichannel/Vector-valued, Deep Learning, Reinforcement Learning, Classification. [bibtex-key = socml2018]



12. Jeremy Kawahara, Colin J. Brown, Steven Miller, Brian G. Booth, Vann Chau, Ruth Grunau, Jill Zwicker, and Ghassan Hamarneh. BrainNetCNN: Artificial Convolutional Neural Networks for Connectomes. In 2nd Annual Health Technology Symposium, Vancouver, Canada, pages 1, 2017. Keyword(s): Diffusion MRI/Tensor-valued, Neurodevelopment, Connectome, Machine Learning, Deep Learning, Classification. [bibtex-key = healthtech2017c]



13. Jeremy Kawahara, Kathleen Moriarty, and Ghassan Hamarneh. Graph Geodesics to Find Progressively Similar Skin Lesion Images. In Medical Image Computing and Computer-Assisted Intervention Workshop on Graphs in Biomedical Image Analysis (MICCAI GRAIL), pages 1-11, 2017. Keyword(s): Machine Learning, Dermatology, Color/Multichannel/Vector-valued, Deep Learning, Graph based. [bibtex-key = miccai_grail2017]



14. Aicha BenTaieb, Jeremy Kawahara, and Ghassan Hamarneh. Multi-Loss Convolutional Networks for Gland Analysis in Microscopy. In IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pages 642-645, 2016. Keyword(s): Machine Learning, Microscopy, Deep Learning. [bibtex-key = isbi2016a]



15. Jeremy Kawahara, Aicha BenTaieb, and Ghassan Hamarneh. Deep Features to Classify Skin Lesions. In IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pages 1397-1400, 2016. Keyword(s): Machine Learning, Dermatology, Deep Learning. [bibtex-key = isbi2016b]



16. Jeremy Kawahara, Colin J. Brown, Steven Miller, Brian G. Booth, Vann Chau, Ruth Grunau, Jill Zwicker, and Ghassan Hamarneh. BrainNetCNN: Artificial Convolutional Neural Networks for Connectomes. In Annual Neuroscience Extravaganza, Centre for Brain Health, BC, Canada, 2016. Keyword(s): Diffusion MRI/Tensor-valued, Neurodevelopment, Connectome, Machine Learning, Deep Learning. [bibtex-key = neuroscix2016a]



17. Jeremy Kawahara and Ghassan Hamarneh. Multi-Resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers. In Medical Image Computing and Computer-Assisted Intervention Workshop on Machine Learning in Medical Imaging (MICCAI MLMI), volume 10019, pages 164-171, 2016. Keyword(s): Machine Learning, Dermatology, Color/Multichannel/Vector-valued, Deep Learning. [bibtex-key = miccai_mlmi2016a]



18. Jeremy Kawahara and Ghassan Hamarneh. Image Content-Based Navigation of Skin Conditions. In World Congress of Dermatology (WCD), 2015. Keyword(s): Dermatology, Color/Multichannel/Vector-valued, Visualization, User Interaction. [bibtex-key = wcd2015a]



19. Ghassan Hamarneh, Alborz Amir-Khalili, Masoud Nosrati, Ivan Figueroa, Jeremy Kawahara, Osama Al-Alao, Jean-Marc Peyrat, Julien Abi-Nahed, Abdulla Al-Ansari, and Rafeef Abugharbieh. Towards Multi-Modal Image-Guided Tumour Identification in Robot-Assisted Partial Nephrectomy. In IEEE Middle East Conference on Biomedical Engineering (IEEE MECBME), pages 159-162, 2014. Keyword(s): Image-Guided Surgery, Segmentation, Registration and Matching, Physical Modelling, Multimodal. [bibtex-key = mecbme2014]



20. Jeremy Kawahara, Jean-Marc Peyrat, Julien Abi-Nahed, Osama Al-Alao, Abdulla Al-Ansari, Rafeef Abugharbieh, and Ghassan Hamarneh. Automatic Labelling of Tumourous vs Tumour-Free Frames in Free-Hand Laparoscopic Ultrasound Video. In Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 8674, pages 677-684, 2014. Keyword(s): Spatio-Temporal, Machine Learning, Segmentation, Image-Guided Surgery. [bibtex-key = miccai2014c]



21. Jeremy Kawahara, Chris McIntosh, Roger Tam, and Ghassan Hamarneh. Augmenting Auto-context with Global Geometric Features for Spinal Cord Segmentation. In Medical Image Computing and Computer-Assisted Intervention Workshop on Machine Learning in Medical Imaging (MICCAI MLMI), volume 8184, pages 212-219, 2013. Keyword(s): Segmentation, Machine Learning, Shape Modelling and Analysis, Multiple Sclerosis, MRI. [bibtex-key = miccai_mlmi2013a]



22. Jeremy Kawahara, Chris McIntosh, Roger Tam, and Ghassan Hamarneh. Globally Optimal Spinal Cord Segmentation using A Minimal Path in High Dimensions. In IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pages 836-839, 2013. Keyword(s): Segmentation, Optimization, Anatomical Trees and Tubular Structures, Graph based, Multiple Sclerosis, MRI. [bibtex-key = isbi2013b]



23. Jeremy Kawahara, Chris McIntosh, Roger Tam, and Ghassan Hamarneh. Novel Morphological and Appearance Features for Predicting Physical Disability from MR Images in Multiple Sclerosis Patients. In Medical Image Computing and Computer-Assisted Intervention Workshop on Computational Methods and Clinical Applications for Spine Imaging (MICCAI CSI), pages 1-13, 2013. Keyword(s): Machine Learning, Shape Modelling and Analysis, Multiple Sclerosis, MRI. [bibtex-key = miccai_csi2013]

1. Ashish Sinha, Jeremy Kawahara, Arezou Pakzad, Kumar Abhishek, Matthieu Ruthven, Enjie Ghorbel, Anis Kacem, Djamila Aouada, and Ghassan Hamarneh. DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images (Sinha, Kawahara, Pakzad: Joint first authors). Technical report arxiv.2305.12621, 5 2023. Keyword(s): Dermatology, Machine Learning, Deep Learning, Segmentation, Synthesis/Simulation/Augmentation, Datasets. [bibtex-key = arxiv:2305.12621]



2. Mengliu Zhao, Jeremy Kawahara, Sajjad Shamanian, Kumar Abhishek, Priyanka Chandrashekar, and Ghassan Hamarneh. Detection and Longitudinal Tracking of Pigmented Skin Lesions in 3D Total-Body Skin Textured Meshes (Zhao and Kawahara: Joint first authors). Technical report arXiv:2105.00374, 5 2021. Keyword(s): Dermatology, Machine Learning, Deep Learning, Optimization, Registration and Matching, Graph based, Tracking. [bibtex-key = arxiv:2105.00374]



3. Kumar Abhishek, Jeremy Kawahara, and Ghassan Hamarneh. Predicting the Clinical Management of Skin Lesions Using Deep Learning. Technical report medRxiv 2020.11.02.20223941, November 2020. Keyword(s): Dermatology, Machine Learning, Deep Learning, Clinical Management. [bibtex-key = medrxiv2020_20223941]



4. Jeremy Kawahara and Ghassan Hamarneh. Visual Diagnosis of Dermatological Disorders: Human and Machine Performance. Technical report arxiv:1906.01256, 6 2019. Keyword(s): Survey/Review, Color/Multichannel/Vector-valued, Machine Learning, Deep Learning, Dermatology, Classification. [bibtex-key = arxiv:1906.01256]



5. Jeremy Kawahara and Ghassan Hamarneh. Fully Convolutional Networks to Detect Clinical Dermoscopic Features. Technical report arxiv:1703.04559, 3 2017. Keyword(s): Machine Learning, Deep Learning, Dermatology, Challenge. [bibtex-key = arxiv:1703.04559]

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