T. Tu*, S. Azizi*, D. Driess, M. Schaekermann, M. Amin, P. Chang, A. Carroll, and et al. "Towards generalist biomedical AI." arXiv preprint arXiv:2307.14334 (2023). [*Co-first Author]. [ArXiv]
S. Azizi*, L. Culp*, J. Freyberg, B. Mustafa, S. Baur, and et al. “Robust and Efficient Medical Imaging with Self-supervision,” Nature Biomedical Engineering, (2022). [*Co-first Author]. [ArXiv][Link]
K. Singhal, T. Tu, J. Gottweis, R. Sayres, Ellery Wulczyn, L. Hou, K. Clark, S. Azizi*, A. Karthikesalingam*, V. Natarajan* and et al. "Towards expert-level medical question answering with large language models." arXiv preprint arXiv:2305.09617 (2023). [*Co-senior Author] [ArXiv]
K. Singhal*, S. Azizi*, T. Tu*, S. Mahdavi, J. Wei, H. W. Chung, and et al. “Large Language Models Encode Clinical Knowledge,” Nature, Vol 620 [*Co-first Author]. [ArXiv][Link]
S. Azizi, S. Kornblith, C. Saharia, M. Norouzi, and D. J. Fleet. "Synthetic data from diffusion models improves imagenet classification (2023)." arXiv preprint arXiv:2304.08466. [ArXiv]
I. Ktena, O. Wiles, I. Albuquerque, S. Rebuffi, R Tanno, A. Guha Roy, S. Azizi et al. "Generative models improve fairness of medical classifiers under distribution shifts." arXiv preprint arXiv:2304.09218 (2023). [ArXiv]
K. Dvijotham, J. Winkens, M. Barsbey, S. Ghaisas, N. Pawlowski, R. Stanforth, P. MacWilliams, Z. Ahmed, S. Azizi, and et al. “Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians (CoDoC),” Nature Medicine 29, 2022. [Link]
J. D. Krogue, S. Azizi, F. Tan, I. Flament-Auvigne, T. Brown, M. Plass, R. Reihs et al. "Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning." Nature Communications Medicine 3, no. 1 (2023). [Link]
A.G. Roy*, J. Ren*, S. Azizi, A. Loh, V. Natarajan, B. Mustafa, N. Pawlowski, B. Lakshminarayanan, J. Winkens, and et al. “Does Your Dermatology Classifier Know What It Doesn’t Know? Detecting the Long-Tail of Unseen Conditions.” Medical Image Analysis Journal, October 2021 [* Co-first Author]. (Impact factor – 13.28) [Link]
S. Azizi, B. Mustafa, F. Ryan, Z. Beaver, J. Freyberg, J. Deaton, A. Loh, A. Karthikesalingam, S. Kornblith, T. Chen, V. Natarajan, and M. Norouzi, “Big Self-Supervised Models Advance Medical Image Classification,” International Conference on Computer Vision (ICCV), October 2021. [ArXiv] [Oral-Short] [Poster] [Slides] [Blog Post]
M. Rezaei, F. Soleymani, B. Bischl, and S. Azizi, “Deep Bregman Divergence for Contrastive Learning of Visual Representations,” Under Review, 2021. [ArXiv]
A. Sedghi, M. Pesteie, G. Javadi, S. Azizi, and et. Al, “Deep Neural Maps for Unsupervised Visualization of High Grade Cancer in Prostate Biopsies,” Journal of Computer Assisted Radiology and Surgery (IJCARS): IPCAI’19 special issue, 2019. (Impact factor – 3.53)
S. Asgari, S. Azizi, and et. al, “A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations,” International Conference on Computer Vision and Pattern Recognition (CVPR), 2019. [ArXiv]
B. Pyman, A. Sedghi, S. Azizi, K. Tyryshkin, N. Renwick, and P. Mousavi, “Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier,” In Pacific symposium on Biocomputing (PSB), 2019.
S. Azizi, et al., “Investigating deep recurrent neural networks for prostate cancer detection: analysis of temporal enhanced ultrasound,” IEEE Transactions on Medical Imaging (TMI), 2018. (Impact factor – 13.94)
S. Azizi, et al., “Towards a real-time, open-source system for temporal enhanced ultrasound-guided prostate biopsy,” Journal of Computer Assisted Radiology and Surgery, 2018. (Impact factor – 3.53)
M. Mohrehkesh, S. Azizi and et al, “Hierarchical watermarking framework based on analysis of local complexity variations”, Multimedia Tools and Applications, 2018. (Impact factor – 2.97)
S. Azizi, A. Rajaram, and et al., “3D Tissue Mimicking Biophantoms for Ultrasound Imaging: Bioprinting and Image Analysis,” In SPIE Medical Imaging, 2018. [Runner-up for Young Scientist Award] [Link] [Slides]
S. Azizi, et al., “Learning from noisy label statistics: detecting high grade prostate cancer in ultrasound guided biopsy,” Medical Image Computing and Computer Assisted Interventions (MICCAI), 2018. [Poster]
S. Azizi, et al., “Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations,” Journal of Computer Assisted Radiology and Surgery (IJCARS): MICCAI’16 special issues, 2017. [Winner of Best Paper Award on MICCAI’17] [Link]
S. Azizi, et al., “Transfer learning from RF to B-mode temporal enhanced ultrasound features for prostate cancer detection,” Journal of Computer Assisted Radiology and Surgery, 2017. (Impact factor – 3.53) [Slides]
S. Bayat, S. Azizi, et al., “Investigation of physical properties underlying enhanced temporal ultrasound” IEEE Transaction on Ultrasonics, Ferroelectrics, and Frequency Control (UFFC), 2017. (Impact factor – 3.56)
S. Bayat, F. Imani, C. Gerardo, G. Nir, S. Azizi, and et al., “Tissue mimicking simulations for temporal enhanced ultrasound-based tissue typing,” In SPIE Medical Imaging, 2017.
S. Azizi, et al., “Ultrasound-based detection of prostate cancer using automatic feature selection with deep belief networks,” Medical Image Computing and Computer Assisted Interventions (MICCAI), 2015. [Poster]
S. A. Bitaghsir, N. Karimi, S. Azizi and S. Samavi “Stereo image watermarking method based on binocular just noticeable difference,” In conference on Electrical Engineering (ICEE), IEEE, pp. 33-88, 2014.
S. Azizi, S. Samavi, M. Mohrekesh and S. Shirani, “Cascaded transform space watermarking based on analysis of local entropy variation,” International Conf. on Multimedia and Expo Workshops (ICME), 2013.
S. Azizi, M. Mohrekesh, S. Samavi, “Hybrid image watermarking using local complexity variations,” conference on Electrical Engineering (ICEE), IEEE, pp. 1-6, 2013.
M. Mohrekesh, S. Azizi and S. Samavi, “Accelerating GPU implementation of Contourlet transform,” Iranian conference on Machine Vision and Image Processing (MVIP), IEEE, 2013.
M. Karimi, M. Mohrekesh, S. Azizi and S. Samavi, “Transparent watermarking based on psychovisual properties using neural networks,” Machine Vision and Image Processing (MVIP), IEEE, 2013.