Talk, UC Davis, Davis, California
A joint interview of me and Tiara Abraham (my sister) by the UC Davis Chancellor Gary May on the university podcast, “Face-to-Face”.
Watch podcast here.
UC Davis article here.
Talk, Abhishek Thakur's channel, YouTube
Abstract: A CycleGAN is a variant of the generative adversarial network (GAN) architecture designed to handle unpaired image conversion problems. In this talk, Tanishq will describe what are CycleGANs, what applications they are best suited for, and their pitfalls. Additionally, he will walk through code for model training and inference as part of a code demo. Finally, he will present his own research applying CycleGANs (and related models) to pathology and microscopy.
Bio: Tanishq Abraham is considered a child genius and a prodigy. He graduated high school at 10 years old with a 4.0 GPA and at 11 he obtained 3 college Associate Degrees also with a perfect 4.0 GPA. At 14, he graduated from University of California, Davis as a biomedical engineer with summa cum laude. As an undergrad, he has presented papers at several conferences. At 14, he was the first author on a review paper about smart bio-inspired vesicles and it’s biomedical engineering applications published in IOP Physical Biology. At 16, he is a published book chapter author in the book “Artificial Intelligence and Deep Learning in Pathology”. Now at 17, he is a 2nd year PhD student in biomedical engineering at UC Davis. He has worked for a year in the Levenson lab at UC Davis on applying deep learning to novel microscopy techniques in order to enable digital pathology applications. His recent conference was presenting his PhD research at the ICML2020 Computational Biology workshop. Tanishq has also reviewed few books that includes two science fiction books and recently a machine learning book.
Follow him on Twitter: https://twitter.com/iScienceLuvr
#MachineLearning #DeepLearning #GAN
Talk, ICML Computational Biology Workshop 2020, Conference Online
Abstract: MUSE is a novel slide-free imaging technique for histological examination of tissues that can serve as an alternative to traditional histology. In order to bridge the gap between MUSE and traditional histology, we aim to convert MUSE images to resemble authentic hematoxylin- and eosin-stained (H&E) images. We evaluated four models: a non-machine-learning-based color-mapping unmixing-based tool, CycleGAN, DualGAN, and GANILLA. CycleGAN and GANILLA provided visually compelling results that appropriately transferred H&E style and preserved MUSE content. Based on training an automated critic on real and generated H&E images, we determined that CycleGAN demonstrated the best performance. We have also found that MUSE color inversion may be a necessary step for accurate modality conversion to H&E. We believe that our MUSE-to-H&E model can help improve adoption of novel slide-free methods by bridging a perceptual gap between MUSE imaging and traditional histology.
Talk, Biomedical Engineering Society Annual Meeting 2018, Atlanta, Georgia
Talk, 18th Annual University of California Systemwide Bioengineering Symposium, University of California, Riverside
Presented as part of the Undergraduate Student Capstone Design Competition with teammates (in alphabetical order): Connor Dougherty, Michelle Mao, Ben Price, Sagar Shah.
Talk, Biomedical Engineering Society Annual Meeting 2017, Pheonix, Arizona
Talk, 17th Annual University of California Systemwide Bioengineering Symposium, University of California, Los Angeles
Abstract to be added.
Talk, TEDxSacramento 2013, Sacramento, California
Watch talk here