arXiv Machine Learning
Jun 19
Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysis
arXiv:2606. 19966v1 Announce Type: cross Abstract: Whole-slide images (WSIs) are widely used for computational cancer prognosis.
DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction
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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arXiv Computer Vision
Sep 1
Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction
arXiv:2510.06113v2 Announce Type: replace Abstract: Survival analysis plays a vital role in making clinical decisions. However, the models currently in use are often difficult to interpret, which red...
arXiv Machine Learning
Sep 23
Evidential Fusion Network for Multimodal Survival Prediction under Missing Modalities
The paper introduces the Evidential Missing Modality Survival Fusion (EMMS) model, which predicts survival outcomes using multimodal data even when some modalities are missing. EMMS applies Dempster‑Shafer theory and Gaussian Random Fuzzy Numbers to fuse information, accounting for both aleatoric and epistemic uncertainty and the reliability of each modality. Experiments on four cancer datasets show that EMMS achieves state‑of‑the‑art performance while providing calibrated, interpretable uncertainty estimates without extra computational cost.
arXiv Computer Vision
Sep 2
SlideMix: Enhancing Whole Slide Image Analysis via Multimodal Shuffling
arXiv:2609.00396v1 Announce Type: new Abstract: Histopathological whole slide images (WSIs) are central to cancer diagnosis, but their gigapixel scale, tissue heterogeneity, weak slide-level supervis...
arXiv AI
Sep 1
Towards Accurate and Lightweight Peripheral Neuroblastic Tumor Diagnosis via Contrastive Multi-scale Pathological Image Analysis
The paper introduces CoPath, a lightweight framework for diagnosing peripheral neuroblastic tumors (pNTs) from whole-slide images. CoPath combines CoHisNet, a multi‑scale feature‑fusion network that replaces traditional MLPs with Kolmogorov‑Arnold Network layers for efficient nonlinear modeling, and PathVote, which aggregates patch‑level predictions using pathology‑informed priors. Experiments on a private pNT cohort and the public BreakHis dataset show that CoPath matches or surpasses existing classifiers while reducing computational complexity.
Hugging Face Trending Papers
Jul 6
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.
arXiv Machine Learning
Sep 3
Morphology signal in whole slide image foundation models can automatically triage slides
The paper introduces a pipeline that uses publicly available whole slide image foundation models (FMs) to automatically triage slides by ranking them based on zero‑shot classification predictions. This approach accurately identifies slides containing the most tumor, achieving top‑2 ranking for patients with up to 43 slides across multiple datasets. The study also proposes a ranked evaluation framework to benchmark FM performance in slide triage.
arXiv Computer Vision
Sep 11
PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images
PathoHR is a new pipeline for predicting breast cancer survival from high‑resolution pathological images. It uses a plug‑and‑play Vision Transformer to enhance patch‑wise whole slide image representations, evaluates multiple similarity metrics to optimize feature learning, and shows that smaller, enhanced patches can match or surpass the accuracy of larger raw patches while cutting computational cost. The authors provide experimental evidence that this approach improves both accuracy and efficiency in computational pathology.
arXiv Machine Learning
Jul 24
Adaptive Confidence-weighted Expansion for Trustworthy Multi-Omics Multimodal Fusion
arXiv:2607. 20742v1 Announce Type: new Abstract: Multimodal learning is a robust approach to improve predictive performance in applications such as medical prognosis.
arXiv AI
Jun 17
Probing, Fusion, and Trustworthiness: A Systematic Evaluation of Foundation Model Representations for Multimodal Cancer Analysis
arXiv:2606. 17115v1 Announce Type: cross Abstract: Foundation models (FMs) have emerged as powerful representation extractors for medical data, yet their generalizability to datasets under distribution shift remains underexplored.
arXiv AI
Jun 8
DaX: Learning General Pathology Representations Across Scales
arXiv:2606. 06983v1 Announce Type: cross Abstract: Computational pathology requires visual representations that transfer across diverse clinical endpoints and remain robust to variation in magnification, staining, scanner type, slide preparation, and input resolution.
arXiv AI
Sep 21
MIST: Multimodal Survival Prediction with Genomic-Guided Histology Attention
MIST is a multimodal survival prediction framework that fuses whole-slide images and genomic profiles by representing genomic features as tokens that query histology context tokens derived from a foundation model. The architecture enriches molecular information with histology context before survival prediction, avoiding late-stage merging of separately encoded modalities. Training incorporates discrete-time survival prediction, genomic feature masking, WSI dropout, and contrastive alignment, and demonstrates improved external C-index across colon, renal, lung, and glioblastoma cohorts compared to standard fusion baselines.