mlsanityData to 7 Oct 2026

Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling

arXiv 2005.14480 ↗First posted 29 May 2020cs.CVcited by 35

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Abstract (arXiv)

Localizing thoracic diseases on chest X-ray plays a critical role in clinical practices such as diagnosis and treatment planning. However, current deep learning based approaches often require strong supervision, e.g. annotated bounding boxes, for training such systems, which is infeasible to harvest in large-scale. We present Probabilistic Class Activation Map (PCAM) pooling, a novel global pooling operation for lesion localization with only image-level supervision. PCAM pooling explicitly leverages the excellent localization ability of CAM during training in a probabilistic fashion. Experiments on the ChestX-ray14 dataset show a ResNet-34 model trained with PCAM pooling outperforms state-of-the-art baselines on both the classification task and the localization task. Visual examination on the probability maps generated by PCAM pooling shows clear and sharp boundaries around lesion regions compared to the localization heatmaps generated by CAM. PCAM pooling is open sourced at https://github.com/jfhealthcare/Chexpert.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 8; 2023: 4; 2024: 6; 2025: 3; 2026: 3.

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  1. 2021TorchXRayVision: A library of chest X-ray datasets and modelscited by 131
  2. 2023ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Modelscited by 116
  3. 2022Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillationcited by 45
  4. 2024Modeling Caption Diversity in Contrastive Vision-Language Pretrainingcited by 42
  5. 2023ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMscited by 35
  6. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  7. 2021VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labelscited by 27
  8. 2021Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantificationcited by 21
  9. 2021Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Trainingcited by 17
  10. 2022AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillationcited by 17
  11. 2021Vision Transformer for COVID-19 CXR Diagnosis using Chest X-ray Feature Corpuscited by 12
  12. 2025RadAlign: Advancing Radiology Report Generation with Vision-Language Concept Alignmentcited by 12
23 more
  1. 2022Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutationcited by 10
  2. 2023Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactualscited by 10
  3. 2025MMXU: A Multi-Modal and Multi-X-ray Understanding Dataset for Disease Progressioncited by 9
  4. 2022Federated Learning with Privacy-Preserving Ensemble Attention Distillationcited by 6
  5. 2021Weakly Supervised Thoracic Disease Localization via Disease Maskscited by 5
  6. 2022ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classificationcited by 5
  7. 2024Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features?cited by 4
  8. 2021Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rayscited by 2
  9. 2021Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identificationcited by 2
  10. 2021Severity Quantification and Lesion Localization of COVID-19 on CXR using Vision Transformercited by 2
  11. 2021Image Embedding and Model Ensembling for Automated Chest X-Ray Interpretationcited by 2
  12. 2022Understanding Transfer Learning for Chest Radiograph Clinical Report Generation with Modified Transformer Architecturescited by 1
  13. 2024Secure Federated Learning Approaches to Diagnosing COVID-19cited by 1
  14. 2024Category-wise Fine-Tuning: Resisting Incorrect Pseudo-Labels in Multi-Label Image Classification with Partial Labelscited by 1
  15. 2024Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactualscited by 1
  16. 2026CXR-LT 2026 Challenge: Projection-Aware Multi-Label and Zero-Shot Chest X-Ray Classificationcited by 1
  17. 2021Chest X-Rays Image Classification from beta-Variational Autoencoders Latent Featurescited by 0
  18. 2022CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasoundcited by 0
  19. 2023Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosiscited by 0
  20. 2024More Than Positive and Negative: Communicating Fine Granularity in Medical Diagnosiscited by 0
  21. 2025Deep Learning-Based Breast Cancer Detection in Mammography: A Multi-Center Validation Study in Thai Populationcited by 0
  22. 2026From Classification to Localization and Clinical Validation: Large-Scale Development of a Deep Learning System for Thoracic Disease Detection on Chest Radiographs in Thailandcited by 0
  23. 2026From Image Interpretation to Clinical Reasoning: Upstream Physician-Context-Aware Multimodal Learning with Causal Reinforcement Learningcited by 0