mlsanityData to 14 Oct 2026

Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers

arXiv 2109.05819 ↗First posted 13 Sep 2021eess.IV cs.CV cs.LGcited by 23

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Abstract (arXiv)

Microsatellite instability (MSI) is a tumor phenotype whose diagnosis largely impacts patient care in colorectal cancers (CRC), and is associated with response to immunotherapy in all solid tumors. Deep learning models detecting MSI tumors directly from H&E stained slides have shown promise in improving diagnosis of MSI patients. Prior deep learning models for MSI detection have relied on neural networks pretrained on ImageNet dataset, which does not contain any medical image. In this study, we leverage recent advances in self-supervised learning by training neural networks on histology images from the TCGA dataset using MoCo V2. We show that these networks consistently outperform their counterparts pretrained using ImageNet and obtain state-of-the-art results for MSI detection with AUCs of 0.92 and 0.83 for CRC and gastric tumors, respectively. These models generalize well on an external CRC cohort (0.97 AUC on PAIP) and improve transfer from one organ to another. Finally we show that predictive image regions exhibit meaningful histological patterns, and that the use of MoCo features highlighted more relevant patterns according to an expert pathologist.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 20 of its 46 references that are in the map. First posted on arXiv 13 Sep 2021.

Builds on

20 of 46 references.

References · 20

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8 more
  1. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 9; 2023: 6; 2024: 5; 2025: 2; 2026: 1.

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  1. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 359
  2. 2024Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathologycited by 67
  3. 2022Self-Supervised Vision Transformers Learn Visual Concepts in Histopathologycited by 50
  4. 2023A General-Purpose Self-Supervised Model for Computational Pathologycited by 42
  5. 2025Distilling foundation models for robust and efficient models in digital pathologycited by 36
  6. 2022Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysiscited by 29
  7. 2022Giga-SSL: Self-Supervised Learning for Gigapixel Imagescited by 24
  8. 2024Towards Large-Scale Training of Pathology Foundation Modelscited by 23
  9. 2022Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learningcited by 22
  10. 2022An Aggregation of Aggregation Methods in Computational Pathologycited by 20
  11. 2023Hierarchical discriminative learning improves visual representations of biomedical microscopycited by 12
  12. 2023A Review of Predictive and Contrastive Self-supervised Learning for Medical Imagescited by 10
11 more
  1. 2022Global-Local Self-Distillation for Visual Representation Learningcited by 8
  2. 2023Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learningcited by 7
  3. 2023SLPD: Slide-level Prototypical Distillation for WSIscited by 6
  4. 2024SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classificationcited by 6
  5. 2025Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Studycited by 6
  6. 2023SRATTA : Sample Re-ATTribution Attack of Secure Aggregation in Federated Learningcited by 3
  7. 2024CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H&E stained imagescited by 3
  8. 2022Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Reviewcited by 2
  9. 2022Weakly-supervised learning for image-based classification of primary melanomas into genomic immune subgroupscited by 1
  10. 2024Distilling Local Texture Features for Colorectal Tissue Classification in Low Data Regimescited by 1
  11. 2026Robustifying pathology foundation models via fine-tuningcited by 0