mlsanityData to 7 Oct 2026

Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images

arXiv 2208.07322 ↗First posted 15 Aug 2022cs.CV cs.AIcited by 4

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Abstract (arXiv)

Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations. Often, this approach directly applies "natural image driven" MIL algorithms which overlook the multi-scale (i.e. pyramidal) nature of WSIs. Off-the-shelf MIL algorithms are typically deployed on a single-scale of WSIs (e.g., 20x magnification), while human pathologists usually aggregate the global and local patterns in a multi-scale manner (e.g., by zooming in and out between different magnifications). In this study, we propose a novel cross-scale attention mechanism to explicitly aggregate inter-scale interactions into a single MIL network for Crohn's Disease (CD), which is a form of inflammatory bowel disease. The contribution of this paper is two-fold: (1) a cross-scale attention mechanism is proposed to aggregate features from different resolutions with multi-scale interaction; and (2) differential multi-scale attention visualizations are generated to localize explainable lesion patterns. By training ~250,000 H&E-stained Ascending Colon (AC) patches from 20 CD patient and 30 healthy control samples at different scales, our approach achieved a superior Area under the Curve (AUC) score of 0.8924 compared with baseline models. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 15 of its 34 references that are in the map. First posted on arXiv 15 Aug 2022.

Builds on

15 of 34 references.

References · 15

  1. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  2. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  3. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 473
  4. 2020Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networkscited by 178
  5. 2015Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classificationcited by 159
  6. 2020Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primarycited by 89
  7. 2020Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Imagescited by 81
  8. 2019Deep Learning for Whole Slide Image Analysis: An Overviewcited by 51
  9. 2020Self-Supervision Closes the Gap Between Weak and Strong Supervision in Histologycited by 43
  10. 2019Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathologycited by 36
  11. 2020RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classificationcited by 28
  12. 2021Multi-Class Cell Detection Using Spatial Context Representationcited by 24
3 more
  1. 2018Negative Log Likelihood Ratio Loss for Deep Neural Network Classificationcited by 17
  2. 2020SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classificationcited by 12
  3. 2020AMINN: Autoencoder-based Multiple Instance Neural Network Improves Outcome Prediction of Multifocal Liver Metastasescited by 8

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Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 2.

4 papers.

  1. 2023Cross-scale Multi-instance Learning for Pathological Image Diagnosiscited by 11
  2. 2025A Cytology Dataset for Early Detection of Oral Squamous Cell Carcinomacited by 1
  3. 2023Cross-modality Attention-based Multimodal Fusion for Non-small Cell Lung Cancer (NSCLC) Patient Survival Predictioncited by 0
  4. 2025MsaMIL-Net: An End-to-End Multi-Scale Aware Multiple Instance Learning Network for Efficient Whole Slide Image Classificationcited by 0