mlsanityData to 14 Oct 2026

Rotation Equivariant CNNs for Digital Pathology

arXiv 1806.03962 ↗First posted 8 Jun 2018cs.CV cs.LG stat.MLcited by 422

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Cited by 45 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection. Utilizing recent findings on rotation equivariant CNNs, the proposed model leverages these symmetries in a principled manner. We present a visual analysis showing improved stability on predictions, and demonstrate that exploiting rotation equivariance significantly improves tumor detection performance on a challenging lymph node metastases dataset. We further present a novel derived dataset to enable principled comparison of machine learning models, in combination with an initial benchmark. Through this dataset, the task of histopathology diagnosis becomes accessible as a challenging benchmark for fundamental machine learning research.

Key facts. Cited by 422 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jun 2018.

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Citing papers per year: 2021: 35; 2022: 63; 2023: 85; 2024: 88; 2025: 76; 2026: 75.

422 papers.

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  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2022Visual Prompt Tuningcited by 1,681
  3. 2023Vision-Language Models for Vision Tasks: A Surveycited by 839
  4. 2023EVA-CLIP: Improved Training Techniques for CLIP at Scalecited by 798
  5. 2021Scaling Vision with Sparse Mixture of Expertscited by 747
  6. 2021FLAVA: A Foundational Language And Vision Alignment Modelcited by 649
  7. 2023DataComp: In search of the next generation of multimodal datasetscited by 644
  8. 2023BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairscited by 520
  9. 2021SLIP: Self-supervision meets Language-Image Pre-trainingcited by 436
  10. 2023EVA-02: A Visual Representation for Neon Genesiscited by 381
  11. 2022Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuningcited by 294
  12. 2023Quilt-1M: One Million Image-Text Pairs for Histopathologycited by 194
38 more
  1. 2021Combined Scaling for Zero-shot Transfer Learningcited by 182
  2. 2024Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathologycited by 161
  3. 2022Finetune like you pretrain: Improved finetuning of zero-shot vision modelscited by 150
  4. 2022Benchmarking Self-Supervised Learning on Diverse Pathology Datasetscited by 150
  5. 2024Task Singular Vectors: Reducing Task Interference in Model Mergingcited by 147
  6. 2022ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Modelscited by 141
  7. 2021The Benchmark Lotterycited by 136
  8. 2022Neural Prompt Searchcited by 135
  9. 2022FacT: Factor-Tuning for Lightweight Adaptation on Vision Transformercited by 133
  10. 2024Localizing Task Information for Improved Model Merging and Compressioncited by 131
  11. 2022Convolutional Bypasses Are Better Vision Transformer Adapterscited by 121
  12. 2024Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Modelscited by 120
  13. 2022Understanding Zero-Shot Adversarial Robustness for Large-Scale Modelscited by 117
  14. 2024Scalable Pre-training of Large Autoregressive Image Modelscited by 115
  15. 2022Chinese CLIP: Contrastive Vision-Language Pretraining in Chinesecited by 109
  16. 2024Multimodal Autoregressive Pre-training of Large Vision Encoderscited by 109
  17. 2024HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysiscited by 107
  18. 2022AUC Maximization in the Era of Big Data and AI: A Surveycited by 100
  19. 2024EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameterscited by 99
  20. 2022Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIPcited by 97
  21. 2025No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspacescited by 96
  22. 2023Virchow: A Million-Slide Digital Pathology Foundation Modelcited by 90
  23. 2022Reliability of CKA as a Similarity Measure in Deep Learningcited by 87
  24. 2022AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilitiescited by 85
  25. 2022Head2Toe: Utilizing Intermediate Representations for Better Transfer Learningcited by 83
  26. 2022Parameter-efficient Model Adaptation for Vision Transformerscited by 81
  27. 2022Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervisioncited by 80
  28. 2022K-LITE: Learning Transferable Visual Models with External Knowledgecited by 79
  29. 2023ALIP: Adaptive Language-Image Pre-training with Synthetic Captioncited by 70
  30. 2023VeCLIP: Improving CLIP Training via Visual-enriched Captionscited by 70
  31. 2021A Petri Dish for Histopathology Image Analysiscited by 64
  32. 2022What Makes Transfer Learning Work For Medical Images: Feature Reuse & Other Factorscited by 61
  33. 2023LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matchingcited by 60
  34. 2023Learning Customized Visual Models with Retrieval-Augmented Knowledgecited by 58
  35. 2022The Lie Derivative for Measuring Learned Equivariancecited by 54
  36. 2021Lossy Compression for Lossless Predictioncited by 52
  37. 2023PathLDM: Text conditioned Latent Diffusion Model for Histopathologycited by 52
  38. 2024Does equivariance matter at scale?cited by 52

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