# Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling

arXiv: https://arxiv.org/abs/2005.14480 · HTML: https://mlsanity.com/paper/2005.14480/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2020.

## Built on by (most cited first, 30 of 36)

- [TorchXRayVision: A library of chest X-ray datasets and models](https://mlsanity.com/paper/2111.00595/) (2021)
- [ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models](https://mlsanity.com/paper/2302.07257/) (2023)
- [Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation](https://mlsanity.com/paper/2209.04599/) (2022)
- [Modeling Caption Diversity in Contrastive Vision-Language Pretraining](https://mlsanity.com/paper/2405.00740/) (2024)
- [ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs](https://mlsanity.com/paper/2305.15964/) (2023)
- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels](https://mlsanity.com/paper/2102.11467/) (2021)
- [Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification](https://mlsanity.com/paper/2104.07235/) (2021)
- [Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training](https://mlsanity.com/paper/2111.01338/) (2021)
- [AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation](https://mlsanity.com/paper/2202.06431/) (2022)
- [Vision Transformer for COVID-19 CXR Diagnosis using Chest X-ray Feature Corpus](https://mlsanity.com/paper/2103.07055/) (2021)
- [RadAlign: Advancing Radiology Report Generation with Vision-Language Concept Alignment](https://mlsanity.com/paper/2501.07525/) (2025)
- [Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation](https://mlsanity.com/paper/2204.03500/) (2022)
- [Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals](https://mlsanity.com/paper/2303.00500/) (2023)
- [MMXU: A Multi-Modal and Multi-X-ray Understanding Dataset for Disease Progression](https://mlsanity.com/paper/2502.11651/) (2025)
- [Federated Learning with Privacy-Preserving Ensemble Attention Distillation](https://mlsanity.com/paper/2210.08464/) (2022)
- [Weakly Supervised Thoracic Disease Localization via Disease Masks](https://mlsanity.com/paper/2101.09915/) (2021)
- [ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification](https://mlsanity.com/paper/2210.02998/) (2022)
- [Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features?](https://mlsanity.com/paper/2402.00912/) (2024)
- [Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays](https://mlsanity.com/paper/2102.09475/) (2021)
- [Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification](https://mlsanity.com/paper/2102.13269/) (2021)
- [Severity Quantification and Lesion Localization of COVID-19 on CXR using Vision Transformer](https://mlsanity.com/paper/2103.07062/) (2021)
- [Image Embedding and Model Ensembling for Automated Chest X-Ray Interpretation](https://mlsanity.com/paper/2105.02966/) (2021)
- [Understanding Transfer Learning for Chest Radiograph Clinical Report Generation with Modified Transformer Architectures](https://mlsanity.com/paper/2205.02841/) (2022)
- [Secure Federated Learning Approaches to Diagnosing COVID-19](https://mlsanity.com/paper/2401.12438/) (2024)
- [Category-wise Fine-Tuning: Resisting Incorrect Pseudo-Labels in Multi-Label Image Classification with Partial Labels](https://mlsanity.com/paper/2401.16991/) (2024)
- [Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals](https://mlsanity.com/paper/2406.05477/) (2024)
- [CXR-LT 2026 Challenge: Projection-Aware Multi-Label and Zero-Shot Chest X-Ray Classification](https://mlsanity.com/paper/2604.02185/) (2026)
- [Chest X-Rays Image Classification from beta-Variational Autoencoders Latent Features](https://mlsanity.com/paper/2109.14760/) (2021)
- [CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound](https://mlsanity.com/paper/2206.08524/) (2022)

## 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.