14 citations · 20 across the 5 of their papers we have counts for
5 papers
Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation
Jiabo Ma, Zhengrui Guo, Fengtao Zhou +20
Foundation models pretrained on large-scale datasets are revolutionizing the field of computational pathology (CPath). The generalization ability of foundation models is crucial fo…
Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes
Tianwei Zhang, Dong Wei, Mengmeng Zhu +2
Self-supervised learning has emerged as a powerful tool for pretraining deep networks on unlabeled data, prior to transfer learning of target tasks with limited annotation. The rel…
Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical Perspective
Yu Cai, Hao Chen, Kwang-Ting Cheng
Medical anomaly detection aims to identify abnormal findings using only normal training data, playing a crucial role in health screening and recognizing rare diseases. Reconstructi…
More complex encoder is not all you need
Weibin Yang, Longwei Xu, Pengwei Wang +4
U-Net and its variants have been widely used in medical image segmentation. However, most current U-Net variants confine their improvement strategies to building more complex encod…
Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays
Yu Cai, Hao Chen, Xin Yang +2
Chest X-ray (CXR) is the most typical radiological exam for diagnosis of various diseases. Due to the expensive and time-consuming annotations, detecting anomalies in CXRs in an un…