5 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…
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
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 o…