# Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images

arXiv: https://arxiv.org/abs/2007.00748 · HTML: https://mlsanity.com/paper/2007.00748/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 15 of 15)

- [Segment Anything Model for Medical Images?](https://mlsanity.com/paper/2304.14660/) (2023)
- [Knowledge Boosting: Rethinking Medical Contrastive Vision-Language Pre-Training](https://mlsanity.com/paper/2307.07246/) (2023)
- [Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning](https://mlsanity.com/paper/2304.04902/) (2023)
- [GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images](https://mlsanity.com/paper/2107.06442/) (2021)
- [Improving Disease Classification Performance and Explainability of Deep Learning Models in Radiology with Heatmap Generators](https://mlsanity.com/paper/2207.00157/) (2022)
- [CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation](https://mlsanity.com/paper/2102.10484/) (2021)
- [Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training](https://mlsanity.com/paper/2401.08469/) (2024)
- [Weakly-Supervised Learning via Multi-Lateral Decoder Branching for Tool Segmentation in Robot-Assisted Cardiovascular Catheterization](https://mlsanity.com/paper/2404.07594/) (2024)
- [ToNNO: Tomographic Reconstruction of a Neural Network's Output for Weakly Supervised Segmentation of 3D Medical Images](https://mlsanity.com/paper/2404.13103/) (2024)
- [INSIGHT: Explainable Weakly-Supervised Medical Image Analysis](https://mlsanity.com/paper/2412.02012/) (2024)
- [CLARiTy: A Vision Transformer for Multi-Label Classification and Weakly-Supervised Localization of Chest X-ray Pathologies](https://mlsanity.com/paper/2512.16700/) (2025)
- [Weighing Features of Lung and Heart Regions for Thoracic Disease Classification](https://mlsanity.com/paper/2105.12430/) (2021)
- [MVC: A Multi-Task Vision Transformer Network for COVID-19 Diagnosis from Chest X-ray Images](https://mlsanity.com/paper/2310.00418/) (2023)
- [Text-Driven Weakly Supervised OCT Lesion Segmentation with Structural Guidance](https://mlsanity.com/paper/2411.12615/) (2024)
- [Hide-and-Seek Attribution: Weakly Supervised Segmentation of Vertebral Metastases in CT](https://mlsanity.com/paper/2512.06849/) (2025)

## Abstract (arXiv)

Deep Convolutional Neural Networks have proven effective in solving the task of semantic segmentation. However, their efficiency heavily relies on the pixel-level annotations that are expensive to get and often require domain expertise, especially in medical imaging. Weakly supervised semantic segmentation helps to overcome these issues and also provides explainable deep learning models. In this paper, we propose a novel approach to the semantic segmentation of medical chest X-ray images with only image-level class labels as supervision. We improve the disease localization accuracy by combining three approaches as consecutive steps. First, we generate pseudo segmentation labels of abnormal regions in the training images through a supervised classification model enhanced with a regularization procedure. The obtained activation maps are then post-processed and propagated into a second classification model-Inter-pixel Relation Network, which improves the boundaries between different object classes. Finally, the resulting pseudo-labels are used to train a proposed fully supervised segmentation model. We analyze the robustness of the presented method and test its performance on two distinct datasets: PASCAL VOC 2012 and SIIM-ACR Pneumothorax. We achieve significant results in the segmentation on both datasets using only image-level annotations. We show that this approach is applicable to chest X-rays for detecting an anomalous volume of air in the pleural space between the lung and the chest wall. Our code has been made publicly available.