arXiv Machine Learning June 2, 2026 By Yucheng Xing, Ling Huang, Jingying Ma, Ruping Hong, Jiangdong Qiu, Pei Liu, Kai He, Huazhu Fu, Mengling Feng

DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction

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arXiv:2510. 00053v2 Announce Type: replace-cross Abstract: Pathology whole-slide images (WSIs) are widely used for cancer survival analysis because of their comprehensive histopathological information at both cellular and tissue levels, enabling quantitative, large-scale, and prognostically rich tumor feature analysis.

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safety

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Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysis

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nlp benchmarks

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multimodal safety

arXiv Machine Learning

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multimodal benchmarks

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By Chad Wong, Sicheng Chen, Tianyi Zhang, Enhui Chai, Yueming Jin, Zeyu Liu, Fei Xia

llms rag multimodal

arXiv AI

Sep 1

Towards Accurate and Lightweight Peripheral Neuroblastic Tumor Diagnosis via Contrastive Multi-scale Pathological Image Analysis

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llms

Hugging Face Trending Papers

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MergeSurv: Merging-Based Continual Learning for Survival Analysis on Whole-Slide Images

Survival analysis on Whole Slide Images (WSIs) is important in computational pathology for prognosis estimation and treatment planning. However, existing survival models are typically trained independently for each cancer cohort, making continual adaptation computationally expensive for gigapixel-scale WSIs.

fine-tuning multimodal safety