328 citations · 344 across the 6 of their papers we have counts for
6 papers · 1 filter
Lamps: Learning Anatomy from Multiple Perspectives via Self-supervision in Chest Radiographs
Ziyu Zhou, Haozhe Luo, Mohammad Reza Hosseinzadeh Taher +4
Foundation models have been successful in natural language processing and computer vision because they are capable of capturing the underlying structures (foundation) of natural la…
Foundation X: Integrating Classification, Localization, and Segmentation through Lock-Release Pretraining Strategy for Chest X-ray Analysis
Nahid Ul Islam, DongAo Ma, Jiaxuan Pang +3
Developing robust and versatile deep-learning models is essential for enhancing diagnostic accuracy and guiding clinical interventions in medical imaging, but it requires a large a…
ACE: Anatomically Consistent Embeddings in Composition and Decomposition
Ziyu Zhou, Haozhe Luo, Mohammad Reza Hosseinzadeh Taher +4
Medical images acquired from standardized protocols show consistent macroscopic or microscopic anatomical structures, and these structures consist of composable/decomposable organs…
Learning Anatomically Consistent Embedding for Chest Radiography
Ziyu Zhou, Haozhe Luo, Jiaxuan Pang +3
Self-supervised learning (SSL) approaches have recently shown substantial success in learning visual representations from unannotated images. Compared with photographic images, med…
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…
Models Genesis
Zongwei Zhou, Vatsal Sodha, Jiaxuan Pang +2
Transfer learning from natural images to medical images has been established as one of the most practical paradigms in deep learning for medical image analysis. To fit this paradig…