Michael B. Gotway — accepted AI-conference papers

2024

Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability Composability and Decomposability from Anatomy via Self Supervision

Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway, Jianming Liang

CVPR 2024poster

Humans effortlessly interpret images by parsing them into part-whole hierarchies; deep learning excels in learning multi-level feature spaces but they often lack explicit coding of part-whole relations a prominent property of medical imaging. To overcome this limitation we introduce Adam-v2 a new se…

Cited by 8 Source ↗PDF ↗code ↗Scholar ↗

2022

DiRA: Discriminative, Restorative, and Adversarial Learning for Self-Supervised Medical Image Analysis

Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway, Jianming Liang

CVPR 2022poster

Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing efforts, however, omit their synergistic effects on each other in a ternary setup, which, we envision, can significantl…

Cited by 112 PDF ↗code ↗Scholar ↗

2019

Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization

Md Mahfuzur Rahman Siddiquee, Zongwei Zhou, Nima Tajbakhsh, Ruibin Feng, Michael B. Gotway, Yoshua Bengio +1

ICCV 2019poster

Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs raises an interesting question: can we train a GAN to remove an object, if present, from an image while otherwise preserving the image? Specifically, can a GAN…

Cited by 120 PDF ↗code ↗Scholar ↗