14 citations · 20 across the 5 of their papers we have counts for
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eess.IV2024★ 5 cited
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…
cs.CV2024★ 14 cited
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…
cs.LG2024★ 1 cited
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…