ArticleComputers in biology and medicine2021
A multi-resolution model for histopathology image classification and localization with multiple instance learning.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Melanoma Intelligence: Explainable AI Reveals Histopathologic Aggressiveness as the Dominant Axis of Lymph-Node Metastasis.Journal of clinical medicine · 2026Article
- CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification.Medical image analysis · 2025Article
- Generating 2.5D pathology for enhanced viewing and AI diagnosis.Journal of pathology informatics · 2025Article
- TRUSWorthy: toward clinically applicable deep learning for confident detection of prostate cancer in micro-ultrasound.International journal of computer assisted radiology and surgery · 2025Article
- Understanding the Impact of Deep Learning Model Parameters on Breast Cancer Histopathological Classification Using ANOVA.Cancers · 2025Article
- Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.Journal of imaging informatics in medicine · 2025Article
- Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types.Scientific reports · 2025Article
- Machine learning methods for histopathological image analysis: Updates in 2024.Computational and structural biotechnology journal · 2025Review
- Multiple Instance Learning for WSI: A comparative analysis of attention-based approaches.Journal of pathology informatics · 2024Article
- A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer.Journal of pathology informatics · 2024Article
- Explainable AI for computational pathology identifies model limitations and tissue biomarkers.ArXiv · 2024Article
- Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning.ArXiv · 2024Article
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- Multiple-instance learning of somatic mutations for the classification of tumour type and the prediction of microsatellite status.Nature biomedical engineering · 2024Article
- Neighborhood attention transformer multiple instance learning for whole slide image classification.Frontiers in oncology · 2024Article
- Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images.Computers in biology and medicine · 2023Article
- Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images.Scientific reports · 2023Article
- Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis.Journal of personalized medicine · 2023Article
- Risk-aware survival time prediction from whole slide pathological images.Scientific reports · 2022Article
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7 authors.
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Abstract
Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra-observer agreement. Most previous work on whole slide image analysis has focused on classification or segmentation of small pre-selected regions-of-interest, which requires fine-grained annotation and is non-trivial to extend for large-scale whole slide analysis. In this paper, we proposed a multi-resolution multiple instance learning model that leverages saliency maps to detect suspicious regions for fine-grained grade prediction. Instead of relying on expensive region- or pixel-level annotations, our model can be trained end-to-end with only slide-level labels. The model is developed on a large-scale prostate biopsy dataset containing 20,229 slides from 830 patients. The model achieved 92.7% accuracy, 81.8% Cohen's Kappa for benign, low grade (i.e. Grade group 1) and high grade (i.e. Grade group ≥ 2) prediction, an area under the receiver operating characteristic curve (AUROC) of 98.2% and an average precision (AP) of 97.4% for differentiating malignant and benign slides. The model obtained an AUROC of 99.4% and an AP of 99.8% for cancer detection on an external dataset.
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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.