72 citations · 74 across the 3 of their papers we have counts for
3 papers
cs.CV2026
A data- and compute-efficient chest X-ray foundation model beyond aggressive scaling
Chong Wang, Yabin Zhang, Yunhe Gao +9
Foundation models for medical imaging are typically pretrained on increasingly large datasets, following a "scale-at-all-costs" paradigm. However, this strategy faces two critical…
cs.CV2021★ 2 cited
CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays
Emma Chen, Andy Kim, Rayan Krishnan +3
A major obstacle to the integration of deep learning models for chest x-ray interpretation into clinical settings is the lack of understanding of their failure modes. In this work,…
eess.IV2021★ 72 cited
Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation
Rikiya Yamashita, Jin Long, Snikitha Banda +2
Suboptimal generalization of machine learning models on unseen data is a key challenge which hampers the clinical applicability of such models to medical imaging. Although various…