The paper introduces Prototype Purification and Regulation (PPR), a multi‑label few‑shot learning framework for medical image classification that addresses two key limitations of existing metric‑based meta‑learning methods. PPR first purifies prototypes by using sample‑level comorbidity scores to highlight disease‑specific features, then regulates inter‑class prototype distances with disease‑level comorbidity statistics to create a comorbidity‑aware embedding space. Experiments on four chest X‑ray datasets, including cross‑domain tests, show that PPR outperforms state‑of‑the‑art methods, improving disease detection and demonstrating robust generalization and clinical applicability.
By Ying-Chih Lin, Po-Chih Kuo, Yong-Sheng Chen
The paper introduces a denoising mutual knowledge distillation framework for bi-directional multiple instance learning (MIL) applied to whole slide image classification in digital pathology. By integrating instance-level classifiers and masks, the method grounds predictions on supporting patches while mitigating noisy labels. The proposed algorithm enhances both bag- and instance-level performance, as demonstrated on public pathology datasets.
By Chen Shu, Boyu Fu, Yiman Li, Ting Yin, Wenchuan Zhang, Jie Chen, Yuhao Yi, Hong Bu
arXiv:2608. 04147v1 Announce Type: cross Abstract: Label noise is common in medical imaging datasets due to factors such as inter-rater variability, annotation errors, and ambiguous cases.
By Abhishek Moturu, Babak Taati, Anna Goldenberg
arXiv:2505. 11034v2 Announce Type: replace-cross Abstract: Robust machine learning depends on clean data, yet current image data cleaning benchmarks rely on synthetic noise or narrow human studies, limiting comparison and real-world relevance.
By Fabian Gr\"oger, Simone Lionetti, Philippe Gottfrois, Alvaro Gonzalez-Jimenez, Ludovic Amruthalingam, Elisabeth Victoria Goessinger, Hanna Lindemann, Marie Bargiela, Marie Hofbauer, Omar Badri, Philipp Tschandl, Arash Koochek, Matthew Groh, Alexander A. Navarini, Marc Pouly
arXiv:2602. 06938v2 Announce Type: replace-cross Abstract: The classification performance of deep neural networks relies strongly on access to large, accurately annotated datasets.
By Julia Werner, Julius Oexle, Oliver Bause, Maxime Le Floch, Franz Brinkmann, Hannah Tolle, Jochen Hampe, Oliver Bringmann
The paper introduces Similarity-as-Evidence (SaE), a framework that calibrates overconfident Vision‑Language Models (VLMs) for medical active learning by reinterpreting text‑image similarity vectors as evidence and modeling them with a Dirichlet distribution. SaE’s Similarity Evidence Head (SEH) captures uncertainty through vacuity and dissonance, enabling a dual‑factor acquisition strategy that prioritizes high‑vacuity samples early and high‑dissonance samples later. Experiments on ten public medical imaging datasets demonstrate state‑of‑the‑art macro‑averaged accuracy (82.57%) and improved calibration (NLL = 0.425) under a 20% label budget.
By Zhuofan Xie, Zishan Lin, Jinliang Lin, Jie Qi, Shaohua Hong, Shuo Li