# Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels

arXiv: https://arxiv.org/abs/1905.06362 · HTML: https://mlsanity.com/paper/1905.06362/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2019.

## Built on by (most cited first, 4 of 4)

- [Deep Learning-based Patient Re-identification Is able to Exploit the Biometric Nature of Medical Chest X-ray Data](https://mlsanity.com/paper/2103.08562/) (2021)
- [A Deep Learning Technique using a Sequence of Follow Up X-Rays for Disease classification](https://mlsanity.com/paper/2203.15060/) (2022)
- [Comparative study of Deep Learning Models for Binary Classification on Combined Pulmonary Chest X-ray Dataset](https://mlsanity.com/paper/2309.10829/) (2023)
- [Taxlifier: Leveraging Disease Taxonomy for Enhanced Multi-Label Classification in Chest Radiography](https://mlsanity.com/paper/2607.05628/) (2026)

## Abstract (arXiv)

Chest X-ray (CXR) is the most common X-ray examination performed in daily clinical practice for the diagnosis of various heart and lung abnormalities. The large amount of data to be read and reported, with 100+ studies per day for a single radiologist, poses a challenge in maintaining consistently high interpretation accuracy. In this work, we propose a method for the classification of different abnormalities based on CXR scans of the human body. The system is based on a novel multi-task deep learning architecture that in addition to the abnormality classification, supports the segmentation of the lungs and heart and classification of regions where the abnormality is located. We demonstrate that by training these tasks concurrently, one can increase the classification performance of the model. Experiments were performed on an extensive collection of 297,541 chest X-ray images from 86,876 patients, leading to a state-of-the-art performance level of 0.883 AUC on average for 12 different abnormalities. We also conducted a detailed performance analysis and compared the accuracy of our system with 3 board-certified radiologists. In this context, we highlight the high level of label noise inherent to this problem. On a reduced subset containing only cases with high confidence reference labels based on the consensus of the 3 radiologists, our system reached an average AUC of 0.945.