mlsanityData to 7 Oct 2026

Cross-scale Multi-instance Learning for Pathological Image Diagnosis

arXiv 2304.00216 ↗First posted 1 Apr 2023eess.IV cs.CV cs.LGcited by 11

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Abstract (arXiv)

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., 20x 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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 19 of its 42 references that are in the map. First posted on arXiv 1 Apr 2023.

Builds on

19 of 42 references.

References · 19

  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. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 357
  5. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 254
  6. 2020Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networkscited by 178
  7. 2015Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classificationcited by 159
  8. 2016Deep Learning for Identifying Metastatic Breast Cancercited by 93
  9. 2020Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Imagescited by 81
  10. 2019Deep Learning for Whole Slide Image Analysis: An Overviewcited by 51
  11. 2020Self-Supervision Closes the Gap Between Weak and Strong Supervision in Histologycited by 43
  12. 2019Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathologycited by 36
7 more
  1. 2021UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia gradingcited by 33
  2. 2020RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classificationcited by 28
  3. 2023Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classificationcited by 27
  4. 2018Negative Log Likelihood Ratio Loss for Deep Neural Network Classificationcited by 17
  5. 2020SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classificationcited by 12
  6. 2020AMINN: Autoencoder-based Multiple Instance Neural Network Improves Outcome Prediction of Multifocal Liver Metastasescited by 8
  7. 2022Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Imagescited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 7; 2026: 2.

11 papers.

  1. 2025Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Studycited by 6
  2. 2025DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Predictioncited by 6
  3. 2024Histopathological Image Classification with Cell Morphology Aware Deep Neural Networkscited by 4
  4. 2024Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learningcited by 2
  5. 2025IPGPhormer: Interpretable Pathology Graph-Transformer for Survival Analysiscited by 1
  6. 2025MAPLE: Multi-scale Attribute-enhanced Prompt Learning for Few-shot Whole Slide Image Classificationcited by 1
  7. 2025CrossFusion: A Multi-Scale Cross-Attention Convolutional Fusion Model for Cancer Survival Predictioncited by 0
  8. 2025Fourier Transform Multiple Instance Learning for Whole Slide Image Classificationcited by 0
  9. 2025Cross-modal ultra-scale learning with tri-modalities of renal biopsy images for glomerular multi-disease auxiliary diagnosiscited by 0
  10. 2026Enabling Progressive Whole-slide Image Analysis with Multi-scale Pyramidal Networkcited by 0
  11. 2026Clinical-Injection Transformer with Domain-Adapted MAE for Lupus Nephritis Prognosis Predictioncited by 0