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arXiv Computer Vision

Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning

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arXiv Machine Learning
Oct 16

Beyond the Dominant Label: Recovering Coexisting Lesion Evidence with Multiple Instance Learning

The paper introduces DomSet-MIL, a multiple instance learning framework designed to recover evidence of coexisting lesions in whole-slide images when only the most severe diagnosis is weakly supervised. It models the dominant-label mechanism through a censored likelihood that treats the reported diagnosis as the maximum severity without assuming other lesions are absent, and employs severity-ordered residual routing with top‑q pooling to preserve secondary findings. Experiments on natural and controlled datasets show that DomSet-MIL outperforms standard MIL approaches in dominant-label prediction while better recovering latent coexisting-lesion evidence.

By Lubin Gan, Jing Zhang, Xinming Zhang, Chengpeng Xu, Siying Wu, Xiaoyan Sun
benchmarks
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arXiv Computer Vision
Sep 28

LadderMIL: Multiple Instance Learning with Coarse-to-Fine Self-Distillation

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
efficiencybenchmarks
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arXiv Machine Learning
Jun 5

In-Context Multiple Instance Learning

arXiv:2606. 06458v1 Announce Type: new Abstract: Multiple Instance Learning (MIL) addresses problems where supervision is available at the level of bags of instances and has been successfully applied in fields ranging from computational pathology to satellite imagery.

By Alexander M\"ollers, Marvin Sextro, Julius Hense, Gabriel Dernbach, Klaus-Robert M\"uller
benchmarks
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arXiv Machine Learning
Jul 8

Reliable Mislabel Detection for Video Capsule Endoscopy Data

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
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arXiv AI
Sep 24

Do Center Biases Propagate? Robustness of Pathology Foundation Models in Whole-Slide Image Classification

The study investigates whether pathology foundation models (PFMs) carry center-related biases into whole-slide image (WSI) classification. By training models with increasing class-center correlations and evaluating six PFMs across four datasets and two MIL aggregators, the authors introduce the Area Under the Cramér's V Curve (AUCC) to measure both accuracy and degradation due to spurious correlations. Results reveal that center information propagates to WSI predictions, with robustness varying by PFM and MIL strategy, and that ComBat harmonization does not consistently improve robustness.

By Il\'an Carretero, Pablo Meseguer, Roc\'io del Amor, Valery Naranjo
computer-visionbenchmarks
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