ArticleMedical image analysis2024
Cross-scale multi-instance learning for pathological image diagnosis.
Article in Medical image analysis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- PathScaleBench: A Multidimensional Benchmark of Cross-Scale Representation Behavior and Magnification-Shift Robustness in Pathology Foundation Models.Journal of imaging informatics in medicine · 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
- CLARISA: Connexin-43 Lateralization Automated ROI-Based Image Signal Analyzer.International journal of molecular sciences · 2026Article
- [Hierarchical attention-driven multiple instance learning for clear cell renal cell carcinoma grading and staging in digital pathology].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- A cell comparative multiple instance learning network guided by image quality assessment for cervical whole slide image classification.iScience · 2026Article
- Interpretable multiple instance learning for hematologic diagnosis from peripheral blood smears.Communications medicine · 2026Article
- Deep learning-based pathomics signature predicts prognosis and treatment response in gastric cancer: a multicenter retrospective study.NPJ precision oncology · 2026Article
- Multitask optimization and convergence stability with hierarchical feature learning for self guided optimization.Scientific reports · 2026Article
- Multiview deep-learning-enabled histopathology for prognostic and therapeutic stratification in stage II colorectal cancer: A retrospective multicenter study.PLoS medicine · 2026Observational
- AI-driven WHO 2021 classification of gliomas based only on H&E-stained slides.Neuro-oncology · 2026Article
- Automated Segmentation and Analysis of Histopathological Breast Cancer Images for Enhanced IDC Diagnosis and Assessment Using MobileNetV2+U-Net With Label Propagation.International journal of breast cancer · 2026Article
- Non-small cell lung cancer subtype classification based on cross-scale multi-instance learning.Scientific reports · 2025Article
- Quantitative benchmarking of anomaly detection methods in digital pathology images.Machine Learning. Health · 2025Article
- Machine-learning models based on histological images from healthy donors identify imageQTLs and predict chronological age.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Self-supervision enhances instance-based multiple instance learning methods in digital pathology: a benchmark study.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Interpretable Multiple Instance Learning for Hematologic Diagnosis from Peripheral Blood Smears.Research square · 2025Article
- Multimodal deep learning model for prediction of breast cancer recurrence risk and correlation with oncotype DX.Breast cancer research : BCR · 2025Article
- Machine Learning Models Based on Histological Images from Healthy Donors Identify ImageQTLs and Predict Chronological Age.bioRxiv : the preprint server for biology · 2025Article
- Unveiling key pathomic features for automated diagnosis and Gleason grade estimation in prostate cancer.BMC medical imaging · 2025Article
- Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning.Nature communications · 2025Article
Corrections and comments
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Authors and funding
15 authors.
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Abstract
Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20× magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.
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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.