ArticleNature biomedical engineering2021
Data-efficient and weakly supervised computational pathology on whole-slide images.
Article in Nature biomedical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 750 papers.
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750 citing papers in PubMed.
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- HistoEncoder: A digital pathology foundation model for prostate cancer.Journal of pathology informatics · 2026Article
- Interpretable prototype learning for EGFR amplification prediction from whole-slide images in glioblastoma.Journal of pathology informatics · 2026Article
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- TiLCA-Net: nuclei segmentation and classification in pathological images via task-specialized attention and efficient decoding.Quantitative imaging in medicine and surgery · 2026Article
- Multi-task deep learning integrating pretreatment MRI and whole slide images predicts induction chemotherapy response and survival in locally advanced nasopharyngeal carcinoma.PLOS digital health · 2026Article
- Pop-Corn: Predicting Perturbation Phenotype Effects Across Single-Cell and Spatial Contexts.bioRxiv : the preprint server for biology · 2026Article
- Targeted Chemical Monitoring and Integrated Preclinical Evaluation of Zishui Qinggan Yin in 4-NQO-Induced Esophageal Precancer: Histopathological and PI3K/AKT/mTOR-Associated Changes.Life (Basel, Switzerland) · 2026Article
- A universal visual foundation model for computational cytopathology.Nature cancer · 2026Article
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- Multimodal Artificial Intelligence in Lung Cancer: From Data Integration to Precision Oncology.Cancers · 2026Review
- Effective tumor kinetics inferred from single routine H&E biopsies enable counterfactual virtual radiotherapy trials.medRxiv : the preprint server for health sciences · 2026Article
- CLEAR-HPV: Interpretable concept discovery for human-papillomavirus-associated morphology in whole-slide histology.Patterns (New York, N.Y.) · 2026Article
- Deep Learning-Based Multi-Cancer Analysis for Predicting Disease-Free Survival Across Multiple Cancer Types.Cancers · 2026Article
- Foundation model-powered deep learning of endometrial histology for predicting the cumulative live birth of an in vitro fertilization cycle.PLOS digital health · 2026Article
690 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and typically suffer from poor domain adaptation and interpretability. Here we report an interpretable weakly supervised deep-learning method for data-efficient WSI processing and learning that only requires slide-level labels. The method, which we named clustering-constrained-attention multiple-instance learning (CLAM), uses attention-based learning to identify subregions of high diagnostic value to accurately classify whole slides and instance-level clustering over the identified representative regions to constrain and refine the feature space. By applying CLAM to the subtyping of renal cell carcinoma and non-small-cell lung cancer as well as the detection of lymph node metastasis, we show that it can be used to localize well-known morphological features on WSIs without the need for spatial labels, that it overperforms standard weakly supervised classification algorithms and that it is adaptable to independent test cohorts, smartphone microscopy and varying tissue content.
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