arXiv Machine Learning

Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology

arXiv:2606. 06224v1 Announce Type: cross Abstract: Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology.

arXiv Machine Learning

23h ago

Pathology Transport: Optimal-Transport Explanations for Clinical Data, and When Their Heatmaps (Fail to) Localize Disease

arXiv:2608. 17370v1 Announce Type: new Abstract: Generative models promise a route to explainable clinical AI: rather than probe a classifier, model the distributions of healthy and diseased patients and read explanations off the geometry between them.

By Lalit Kumar

benchmarks safety

arXiv AI

Jul 7

EvoXplain: When Machine Learning Models Agree on Predictions but Disagree on Why -- Measuring Mechanistic Multiplicity Across Training Runs

arXiv:2512. 22240v5 Announce Type: replace-cross Abstract: Machine learning models are primarily judged by predictive performance, especially in applied genomics, where explanations are read as biological findings.

By Chama Bensmail

safety

arXiv AI

Aug 5

SAGE: Semantic Explainability of Attention-Based Survival Models in Computational Pathology

arXiv:2608. 02803v1 Announce Type: cross Abstract: Attention-based multiple instance learning (ABMIL) is the predominant approach for slide-level prediction in computational pathology, yet its attention maps provide only local explanations: they indicate where a model focuses but not which histological features drive its predictions or how the model behaves across a patient cohort.

By Abdallah Lamane, Abdul Rahman Diab, Ren-Chin Wu, William Lotter

llms multimodal benchmarks

arXiv AI

Jun 15

STaR-DRO: Stateful Tsallis Reweighting for Group-Robust Structured Prediction

arXiv:2604. 09737v2 Announce Type: replace-cross Abstract: Structured prediction with large language models requires outputs that are label-accurate, ontology-constrained, structurally valid, and evidence-grounded under label imbalance and heterogeneous group difficulty.

By Samah Fodeh, Ganesh Puthiaraju, Elyas Irankhah, Afshan Khan, Sreeraj Ramachandran, Linhai Ma, Srivani Talakokkul, Sarah Schellhorn

llms fine-tuning safety

arXiv Machine Learning

Jun 2

OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction

arXiv:2510. 17532v2 Announce Type: replace-cross Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data.

By Raghu Vamshi Hemadri, Geetha Krishna Guruju, Kristi Topollai, Anna Ewa Choromanska

llms nlp reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 28

KANEx: Translating Kolmogorov-Arnold Networks' Interpretability to Medical Explainability

arXiv:2607. 24730v1 Announce Type: cross Abstract: Computer vision models have become highly effective for medical applications, yet their black-box nature continues to undermine clinician trust.

By Krithi Shailya, Ananya Lakshmi Ravi, Venkatanathan K. V., Sowmya S. Sundaram, Gokul S. Krishnan, Aditi Anand, Balaraman Ravindran

llms computer-vision multimodal safety

arXiv AI

Jul 10

Towards Precision Therapy in Hepatocellular Carcinoma: A Clinical-Reasoning LLM for Risk Stratification and Treatment Guidance

arXiv:2607. 08602v1 Announce Type: new Abstract: Hepatocellular carcinoma (HCC) is a common malignancy and a leading cause of cancer-related mortality.

By Peng Cui, Jitao Wang, Siyan Xue, Yao Huang, Haoming Xia, Dong Li, Dengxiang Liu, Weilin Wang, Liping Liu, Leida Zhang, Yunfu Cui, Tao Peng, Daolin Ji, Haitao Zhao, Wei Zhang, Xiaojuan Wang, Weijie Ma, Zongren Ding, Jinlong Li, Yuan Ding, Jiajing Zhao, Zhiyu Chen, Chengkun Yang, Ziyue Huang, Jiaqi Liu, Fusheng Liu, Yang Zhou, Xiaojuan Wang, Zhongquan Sun, Shiyun Bao, Xiaojun Wang, Ming Yang, Guangxin Li, Bin Shu, Yong Liao, Hongxuan Li, Yao Tang, Shizhong Yang, Yongyi Zeng, Yufeng Yuan, Yinpeng Dong, Jihui Hao, Jun Zhu, Jiahong Dong

llms benchmarks

arXiv AI

Jun 30

RADIANT-PET: Reasoning-Augmented PET/CT Lesion Segmentation with Large Language Models and Reinforcement Learning

arXiv:2606. 28392v1 Announce Type: cross Abstract: Accurate lesion segmentation in PET/CT is critical for oncology, yet remains challenging because physiologic tracer uptake and artifacts can mimic malignant signal.

By Jiasheng Wang, Tanun Jitwatcharakomol, Piyawadee Jongpradubgiat, Simeng Zhu

llms computer-vision reinforcement-learning

arXiv AI

Jun 16

Medical Heuristic Learning: An LLM-Driven Framework for Interpretable and Auditable Clinical Decision Rules

arXiv:2606. 16337v1 Announce Type: new Abstract: Predictive modeling for clinical tabular data is central to clinical decision support and therefore requires not only strong predictive performance but also transparent decision logic.

By Wei Xu, Ke Yang, Gang Luo, Keli Zheng, Lingyan Hu, Jing Wang, Kefeng Li

llms benchmarks

arXiv AI

Jun 30

Data-Efficient Multimodal Alignment for Histopathology-based Molecular Prediction

arXiv:2606. 29949v1 Announce Type: cross Abstract: H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution at high cost with limited archival availability.

By Dominik Winter, Dominik Vonficht, Lo\"ic Le Bescond, Christian Gebbe, Marco Rosati, Richard J. Chen, Markus Schick, Ross Stewart, Nicolas Brieu

llms fine-tuning multimodal safety

arXiv AI

Jun 30

Skin-R1: Clinical Knowledge-Guided Dermatological Diagnosis Using Vision-Language Models

arXiv:2511. 14900v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) have recently shown promise for assisting clinical reasoning in dermatological diagnosis.

By Zehao Liu, Weijieying Ren, Jipeng Zhang, Tianxiang Zhao, Jingxi Zhu, Xiaoting Li, Vasant G Honavar

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 29

Revisiting Performance Claims for Chest X-Ray Models Using Clinical Context

arXiv:2509. 19671v3 Announce Type: replace Abstract: Public datasets of Chest X-Rays (CXRs) have long been a popular benchmark for developing machine learning (ML) computer vision models in healthcare.

By Andrew Wang, Jiashuo Zhang, Michael Oberst

computer-vision benchmarks

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