mlsanityData to 7 Oct 2026

PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis

arXiv 2301.00772 ↗First posted 2 Jan 2023cs.CV cs.LGcited by 40

Not on the map. Why?

Abstract (arXiv)

Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g., image-based diagnosis and tumor segmentation). To mitigate the locality problem of comparative SSL, we propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding but has not drawn much attention in SSL. The resulting framework can be formulated as a multi-task optimization problem on the feature pyramid. Specifically, we conduct multi-scale pixel restoration and siamese feature comparison in the pyramid. In addition, we propose non-skip U-Net to build the feature pyramid and develop sub-crop to replace multi-crop in 3D medical imaging. The proposed unified SSL framework (PCRLv2) surpasses its self-supervised counterparts on various tasks, including brain tumor segmentation (BraTS 2018), chest pathology identification (ChestX-ray, CheXpert), pulmonary nodule detection (LUNA), and abdominal organ segmentation (LiTS), sometimes outperforming them by large margins with limited annotations.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 39 of its 50 references that are in the map. First posted on arXiv 2 Jan 2023.

Builds on

39 of 50 references.

References · 39

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  4. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  5. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  6. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  7. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  8. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  9. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  10. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  11. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
  12. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
27 more
  1. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,282
  2. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
  3. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  4. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,424
  5. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  6. 2019Contrastive Multiview Codingcited by 1,297
  7. 2018Learning deep representations by mutual information estimation and maximizationcited by 1,265
  8. 2019Learning Representations by Maximizing Mutual Information Across Viewscited by 733
  9. 2019Data-Efficient Image Recognition with Contrastive Predictive Codingcited by 672
  10. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 504
  11. 2021Big Self-Supervised Models Advance Medical Image Classificationcited by 320
  12. 2020Contrastive learning of global and local features for medical image segmentation with limited annotationscited by 309
  13. 2016Learning Representations for Automatic Colorizationcited by 251
  14. 2016Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Predictioncited by 217
  15. 2016Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challengecited by 198
  16. 2020TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learningcited by 176
  17. 20203D Self-Supervised Methods for Medical Imagingcited by 134
  18. 2021SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentationcited by 86
  19. 2020Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representationscited by 69
  20. 2021Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learningcited by 68
  21. 2021Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contextscited by 59
  22. 2021MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretationcited by 52
  23. 2021Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentationcited by 49
  24. 2020Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentationcited by 40
  25. 2015Multi-scale recognition with DAG-CNNscited by 23
  26. 2021SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddingscited by 21
  27. 2021Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlationcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 11; 2024: 16; 2025: 9; 2026: 4.

40 papers.

Sort:

By area:

  1. 2023Advancing Radiograph Representation Learning with Masked Record Modelingcited by 75
  2. 2023Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentationcited by 47
  3. 2023Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learningcited by 34
  4. 2023Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learningcited by 34
  5. 2024Large-Scale 3D Medical Image Pre-training with Geometric Context Priorscited by 34
  6. 2024Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defensescited by 30
  7. 2023Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Analysiscited by 25
  8. 2024Multi-modal Vision Pre-training for Medical Image Analysiscited by 22
  9. 2023Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencodercited by 19
  10. 2024MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysiscited by 17
  11. 2024Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directionscited by 17
  12. 2023Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentationcited by 16
28 more
  1. 2023ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Promptingcited by 15
  2. 2023T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistencycited by 14
  3. 2023FreMIM: Fourier Transform Meets Masked Image Modeling for Medical Image Segmentationcited by 10
  4. 2025Why LVLMs Are More Prone to Hallucinations in Longer Responses: The Role of Contextcited by 9
  5. 2024MLIP: Medical Language-Image Pre-training with Masked Local Representation Learningcited by 7
  6. 2024MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentationcited by 7
  7. 2024MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Modelcited by 7
  8. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  9. 2024Multimodal self-supervised learning for lesion localizationcited by 4
  10. 2024LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Modelscited by 4
  11. 2024Enhancing Representation in Medical Vision-Language Foundation Models via Multi-Scale Information Extraction Techniquescited by 3
  12. 2024MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learningcited by 3
  13. 2024OTCXR: Rethinking Self-supervised Alignment using Optimal Transport for Chest X-ray Analysiscited by 3
  14. 2024CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learningcited by 2
  15. 2025Learning 3D Medical Image Models From Brain Functional Connectivity Network Supervision For Mental Disorder Diagnosiscited by 2
  16. 2025Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imagingcited by 2
  17. 2025M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervisioncited by 2
  18. 2024Self-supervised learning improves robustness of deep learning lung tumor segmentation to CT imaging differencescited by 1
  19. 2025MedCL: Learning Consistent Anatomy Distribution for Scribble-supervised Medical Image Segmentationcited by 1
  20. 2025Enhancing 3D Medical Image Understanding with Pretraining Aided by 2D Multimodal Large Language Modelscited by 1
  21. 2026ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-trainingcited by 1
  22. 2024GUESS: Generative Uncertainty Ensemble for Self Supervisioncited by 0
  23. 2025Semi-KAN: KAN Provides an Effective Representation for Semi-Supervised Learning in Medical Image Segmentationcited by 0
  24. 2025BioVFM-21M: Benchmarking and Scaling Self-Supervised Vision Foundation Models for Biomedical Image Analysiscited by 0
  25. 2025DiSSECT: Structuring Transfer-Ready Medical Image Representations through Discrete Self-Supervisioncited by 0
  26. 2026M-IDoL: Information Decomposition for Modality-Specific and Diverse Representation Learning in Medical Foundation Modelcited by 0
  27. 2026Benchmarking transferability of SSL pretraining to same and different modality segmentation taskscited by 0
  28. 2026Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelinescited by 0