3.3k citations · 3.5k across the 13 of their papers we have counts for
13 papers
Foundation Ark: Accruing and Reusing Knowledge for Superior and Robust Performance
DongAo Ma, Jiaxuan Pang, Michael B. Gotway +1
Deep learning nowadays offers expert-level and sometimes even super-expert-level performance, but achieving such performance demands massive annotated data for training (e.g., Goog…
Towards Foundation Models Learned from Anatomy in Medical Imaging via Self-Supervision
Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway, Jianming Liang
Human anatomy is the foundation of medical imaging and boasts one striking characteristic: its hierarchy in nature, exhibiting two intrinsic properties: (1) locality: each anatomic…
DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis
Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway +1
Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing…
CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging
Mohammad Reza Hosseinzadeh Taher, Fatemeh Haghighi, Michael B. Gotway +1
Recently, self-supervised instance discrimination methods have achieved significant success in learning visual representations from unlabeled photographic images. However, given th…
Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection
Nahid Ul Islam, Shiv Gehlot, Zongwei Zhou +2
Pulmonary embolism (PE) represents a thrombus ("blood clot"), usually originating from a lower extremity vein, that travels to the blood vessels in the lung, causing vascular obstr…
A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis
Mohammad Reza Hosseinzadeh Taher, Fatemeh Haghighi, Ruibin Feng +2
Transfer learning from supervised ImageNet models has been frequently used in medical image analysis. Yet, no large-scale evaluation has been conducted to benchmark the efficacy of…