arXiv:2607. 12376v1 Announce Type: cross Abstract: Causal inference using front door intervention and multi-instance learning (MIL) has advanced the analysis of Whole Slide Images (WSI) in digital pathology.
By Zhirui Zhang, Tianhang Nan, Yong Ding, Zhuolun Song, Dayu Hu, Xiaoyu Cui
arXiv:2502.02707v5 Announce Type: replace
Abstract: Multiple Instance Learning (MIL) for whole slide image (WSI) analysis in computational pathology often neglects instance-level learning as supervis...
By Shuyang Wu, Yifu Qiu, Ines P. Nearchou, Sandrine Prost, Jonathan A. Fallowfield, Hideki Ueno, Hitoshi Tsuda, David J. Harrison, Hakan Bilen, Timothy J. Kendall
arXiv:2606. 06864v1 Announce Type: cross Abstract: Multiple instance learning (MIL) has become a standard paradigm for whole slide image (WSI) analysis in digital pathology, as it enables slide-level prediction without dense annotations.
By Yonghan Shin, Won-Ki Jeong
arXiv:2610.15669v2 Announce Type: replace-cross
Abstract: Weak bag-level labels are widely available in computational pathology, whereas reliable instance-level annotations for whole-slide images rem...
By Chen Shu, Chengyang Zhang, Boyu Fu, Ting Yin, Wenchuan Zhang, Jie Chen, Yuhao Yi, Hong Bu
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
arXiv:2607. 02572v1 Announce Type: cross Abstract: In multi-source image fusion scenarios, heterogeneous inputs are typically driven by distinct generative mechanisms and can be viewed as a composition of multiple causal systems.
By Zhizhong Fu, Wei Zhou, Zhaoyang Jiang, Yulong Lin, Yifu Hou, Xiaorong Ding, Qiang Yan, Yifan Chen