ArticleComputers in biology and medicine2023
MHAttnSurv: Multi-head attention for survival prediction using whole-slide pathology images.
Article in Computers in biology and medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Interpretable Cancer Survival Prediction by Fusing Semantic Labelling of Cell Types and Whole Slide Images.Interdisciplinary sciences, computational life sciences · 2026Article
- A unified multi modal transformer framework for breast cancer recurrence prediction and survival analysis.Scientific reports · 2026Article
- Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records.The American journal of pathology · 2026Article
- AI in Cancer Prognosis: A Systematic Review of Multimodal Models Combining Pathology Images and High-Throughput Omics.Cancer informatics · 2026Article
- Deep learning for prognostic stratification and biomarker exploration in upper tract urothelial carcinoma: a multicenter retrospective cohort study.International journal of surgery (London, England) · 2026Article
- LIMPACAT: Multi-omics attention transformer for immune prediction in liver cancer using whole-slide imaging.PloS one · 2026Article
- Deep learning for digital pathology: A critical overview of methodological framework.Journal of pathology informatics · 2025Review
- Predicting targeted therapy resistance in non-small cell lung cancer using multimodal machine learning.Journal of thoracic disease · 2025Article
- Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction.IEEE transactions on big data · 2025Article
- MMsurv: a multimodal multi-instance multi-cancer survival prediction model integrating pathological images, clinical information, and sequencing data.Briefings in bioinformatics · 2025Article
- AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology.NPJ digital medicine · 2025Article
- Deep learning detected histological differences between invasive and non-invasive areas of early esophageal cancer.Cancer science · 2025Article
- From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology.Nature protocols · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Masked pre-training of transformers for histology image analysis.Journal of pathology informatics · 2024Article
- Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images.The journal of pathology. Clinical research · 2024Article
- A multi-model approach integrating whole-slide imaging and clinicopathologic features to predict breast cancer recurrence risk.NPJ breast cancer · 2024Article
- Deep Learning for Grading Endometrial Cancer.The American journal of pathology · 2024Article
- Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients.Cell reports methods · 2024Article
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
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3 authors.
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Abstract
Whole slide images (WSI) based survival prediction has attracted increasing interest in pathology. Despite this, extracting prognostic information from WSIs remains a challenging task due to their enormous size and the scarcity of pathologist annotations. Previous studies have utilized multiple instance learning approach to combine information from several randomly sampled patches, but this approach may not be adequate as different visual patterns may contribute unequally to prognosis prediction. In this study, we introduce a multi-head attention mechanism that allows each attention head to independently explore the utility of various visual patterns on a tumor slide, thereby enabling more comprehensive information extraction from WSIs. We evaluated our approach on four cancer types from The Cancer Genome Atlas database. Our model achieved an average c-index of 0.640, outperforming three existing state-of-the-art approaches for WSI-based survival prediction on these datasets. Visualization of attention maps reveals that the attention heads synergistically focus on different morphological patterns, providing additional evidence for the effectiveness of multi-head attention in survival prediction.
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