# Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays

arXiv: https://arxiv.org/abs/1807.06067 · HTML: https://mlsanity.com/paper/1807.06067/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2018.

## Built on by (most cited first, 21 of 21)

- [A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation](https://mlsanity.com/paper/2104.10326/) (2021)
- [Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis](https://mlsanity.com/paper/2112.12349/) (2021)
- [Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-rays](https://mlsanity.com/paper/2106.05915/) (2021)
- [Probabilistic Integration of Object Level Annotations in Chest X-ray Classification](https://mlsanity.com/paper/2210.06980/) (2022)
- [Anatomy-Driven Pathology Detection on Chest X-rays](https://mlsanity.com/paper/2309.02578/) (2023)
- [I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR Diagnoses](https://mlsanity.com/paper/2309.13550/) (2023)
- [BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification](https://mlsanity.com/paper/2203.01937/) (2022)
- [Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment](https://mlsanity.com/paper/2104.05261/) (2021)
- [HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images](https://mlsanity.com/paper/2310.06143/) (2023)
- [ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification](https://mlsanity.com/paper/2210.02998/) (2022)
- [Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling](https://mlsanity.com/paper/2402.11985/) (2024)
- [GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images](https://mlsanity.com/paper/2107.06442/) (2021)
- [Computer-Aided Diagnosis of Thoracic Diseases in Chest X-rays using hybrid CNN-Transformer Architecture](https://mlsanity.com/paper/2404.11843/) (2024)
- [Searching for Pneumothorax in X-Ray Images Using Autoencoded Deep Features](https://mlsanity.com/paper/2102.06096/) (2021)
- [Weighing Features of Lung and Heart Regions for Thoracic Disease Classification](https://mlsanity.com/paper/2105.12430/) (2021)
- [Interpretation of Chest x-rays affected by bullets using deep transfer learning](https://mlsanity.com/paper/2203.13461/) (2022)
- [MVC: A Multi-Task Vision Transformer Network for COVID-19 Diagnosis from Chest X-ray Images](https://mlsanity.com/paper/2310.00418/) (2023)
- [Artificially Generated Visual Scanpath Improves Multi-label Thoracic Disease Classification in Chest X-Ray Images](https://mlsanity.com/paper/2503.00657/) (2025)
- [Improving Imbalanced Multi-Label Chest X-Ray Diagnosis via CBAM-Enhanced CNN Backbones](https://mlsanity.com/paper/2605.02328/) (2026)
- [Taxlifier: Leveraging Disease Taxonomy for Enhanced Multi-Label Classification in Chest Radiography](https://mlsanity.com/paper/2607.05628/) (2026)
- [Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection](https://mlsanity.com/paper/2609.10988/) (2026)

## Abstract (arXiv)

Chest X-rays is one of the most commonly available and affordable radiological examinations in clinical practice. While detecting thoracic diseases on chest X-rays is still a challenging task for machine intelligence, due to 1) the highly varied appearance of lesion areas on X-rays from patients of different thoracic disease and 2) the shortage of accurate pixel-level annotations by radiologists for model training. Existing machine learning methods are unable to deal with the challenge that thoracic diseases usually happen in localized disease-specific areas. In this article, we propose a weakly supervised deep learning framework equipped with squeeze-and-excitation blocks, multi-map transfer, and max-min pooling for classifying thoracic diseases as well as localizing suspicious lesion regions. The comprehensive experiments and discussions are performed on the ChestX-ray14 dataset. Both numerical and visual results have demonstrated the effectiveness of the proposed model and its better performance against the state-of-the-art pipelines.