ArticleConference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops2021
Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning.
Article in Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 202 papers.
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Who cites it
202 citing papers in PubMed.
- A new era in computational pathology: A survey on foundation and vision-language models.Journal of pathology informatics · 2026Review
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- Deep Learning Classification of Uveal Melanoma and Metastatic Carcinoma on Cytology Specimens: A Single-Center Experience.Investigative ophthalmology & visual science · 2026Article
- MeCaMIL: Causality-Aware Multiple Instance Learning for Fair and Interpretable Whole Slide Image Diagnosis.IEEE transactions on medical imaging · 2026Article
- Multimodal Nomogram for the Prenatal Risk Assessment of Hypoplastic Left Heart Syndrome Using Self-Supervised Learning.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- XChondNet: Explainable Chondrogenic Tumor Diagnosis by Spatial-Context-Aware Synergistic Deep Feature Fusion.Sensors (Basel, Switzerland) · 2026Article
- ConMIL: interactive and contrastive text-guided multiple instance learning for whole slide image classification.Bioinformatics (Oxford, England) · 2026Article
- Agentic systems in computational pathology: architectures, evidence, and translational challenges.Journal of translational medicine · 2026Review
- Review
- Grading of Castleman Disease Histopathology with an Attention-Based Multiple Instance Learning Model.Diagnostics (Basel, Switzerland) · 2026Article
- Weakly supervised artificial intelligence for multi-cancer detection of lymph node metastasis on whole slide images.Nature communications · 2026Article
- V7-DiVA: a feature-based Deep MIL colorectal whole-slide histopathology research platform with blinded external image-input validation.Journal of translational medicine · 2026Article
- Prototype-based AI triage for 3D pathology.bioRxiv : the preprint server for biology · 2026Article
- Deep Learning on H&E Pathology Images PredictsDiseases (Basel, Switzerland) · 2026Article
- Article
- An interpretable multi-task whole-slide histopathology AI model for non-small cell lung cancer: Cross-cohort generalisation, spatial attention-transcriptomic integration, and molecular-immune profiling.Clinical and translational medicine · 2026Article
- Frequency-domain multi-scale hybrid attention for pathological image classification.Scientific reports · 2026Article
- A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer.Insights into imaging · 2026Article
- A comparative analysis of deep learning models for disease classification in multi-organ histopathological images.Scientific reports · 2026Article
- Domain generalisation challenges in breast cancer molecular classification using foundation models: a cross-cohort exploratory study.Medical & biological engineering & computing · 2026Article
142 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
We address the challenging problem of whole slide image (WSI) classification. WSIs have very high resolutions and usually lack localized annotations. WSI classification can be cast as a multiple instance learning (MIL) problem when only slide-level labels are available. We propose a MIL-based method for WSI classification and tumor detection that does not require localized annotations. Our method has three major components. First, we introduce a novel MIL aggregator that models the relations of the instances in a dual-stream architecture with trainable distance measurement. Second, since WSIs can produce large or unbalanced bags that hinder the training of MIL models, we propose to use self-supervised contrastive learning to extract good representations for MIL and alleviate the issue of prohibitive memory cost for large bags. Third, we adopt a pyramidal fusion mechanism for multiscale WSI features, and further improve the accuracy of classification and localization. Our model is evaluated on two representative WSI datasets. The classification accuracy of our model compares favorably to fully-supervised methods, with less than 2% accuracy gap across datasets. Our results also outperform all previous MIL-based methods. Additional benchmark results on standard MIL datasets further demonstrate the superior performance of our MIL aggregator on general MIL problems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.