ArticleMedical image analysis2022
Attention2majority: Weak multiple instance learning for regenerative kidney grading on whole slide images.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 51 citations in OpenAlex.
- GenoGlyph: Pan-cancer genomic mutation inference and risk stratification from diagnostic histopathology slides.Research square · 2026Article
- Detection and classification of medical images using deep learning for chronic kidney disease.International urology and nephrology · 2026Article
- TNM staging of esophageal cancer using fine-tuned pathology foundation models and multiple instance learning.Frontiers in oncology · 2026Article
- HistoChat: Instruction-tuning multimodal vision language assistant for colorectal histopathology on limited data.Patterns (New York, N.Y.) · 2025Article
- Permutation-Invariant Cascaded Attentional Set Operator for Computational Nephropathology.Kidney360 · 2025Article
- Benchmarking histopathology foundation models for ovarian cancer bevacizumab treatment response prediction from whole slide images.Discover oncology · 2025Article
- Not seeing the trees for the forest. The impact of neighbours on graph-based configurations in histopathology.BMC bioinformatics · 2025Article
- Weakly supervised pathological differentiation of primary central nervous system lymphoma and glioblastoma on multi-site whole slide images.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Enhanced prediction of gene mutation and risk stratification in non-small-cell lung cancer through dual-pathway fusion of radiomics and pathomics.Frontiers in oncology · 2025Article
- Cross-Attention-Based Saliency Inference for Predicting Cancer Metastasis on Whole Slide Images.IEEE journal of biomedical and health informatics · 2024Article
- Explainable AI for computational pathology identifies model limitations and tissue biomarkers.ArXiv · 2024Article
- Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images.Medical & biological engineering & computing · 2024Article
- Translating prognostic quantification of c-MYC and BCL2 from tissue microarrays to whole slide images in diffuse large B-cell lymphoma using deep learning.Diagnostic pathology · 2024Article
- Article
- One label is all you need: Interpretable AI-enhanced histopathology for oncology.Seminars in cancer biology · 2023Review
- Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images.Computers in biology and medicine · 2023Article
- NRK-ABMIL: Subtle Metastatic Deposits Detection for Predicting Lymph Node Metastasis in Breast Cancer Whole-Slide Images.Cancers · 2023Article
- Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning.Medical image analysis · 2023Article
- BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images.PloS one · 2023Article
- Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.Journal of pathology informatics · 2023Review
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Deep learning consistently demonstrates high performance in classifying and segmenting medical images like CT, PET, and MRI. However, compared to these kinds of images, whole slide images (WSIs) of stained tissue sections are huge and thus much less efficient to process, especially for deep learning algorithms. To overcome these challenges, we present attention2majority, a weak multiple instance learning model to automatically and efficiently process WSIs for classification. Our method initially assigns exhaustively sampled label-free patches with the label of the respective WSIs and trains a convolutional neural network to perform patch-wise classification. Then, an intelligent sampling method is performed in which patches with high confidence are collected to form weak representations of WSIs. Lastly, we apply a multi-head attention-based multiple instance learning model to do slide-level classification based on high-confidence patches (intelligently sampled patches). Attention2majority was trained and tested on classifying the quality of 127 WSIs (of regenerated kidney sections) into three categories. On average, attention2majority resulted in 97.4%±2.4 AUC for the four-fold cross-validation. We demonstrate that the intelligent sampling module within attention2majority is superior to the current state-of-the-art random sampling method. Furthermore, we show that the replacement of random sampling with intelligent sampling in attention2majority results in its performance boost (from 94.9%±3.1 to 97.4%±2.4 average AUC for the four-fold cross-validation). We also tested a variation of attention2majority on the famous Camelyon16 dataset, which resulted in 89.1%±0.8 AUC
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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.