# OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays

arXiv: https://arxiv.org/abs/2104.03218 · HTML: https://mlsanity.com/paper/2104.03218/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 35 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://mlsanity.com/paper/2006.04388/) (2020)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation](https://mlsanity.com/paper/1908.10454/) (2019)
- [Data Distillation: Towards Omni-Supervised Learning](https://mlsanity.com/paper/1712.04440/) (2017)
- [Robust Classification with Convolutional Prototype Learning](https://mlsanity.com/paper/1805.03438/) (2018)
- [Multiple Instance Detection Network with Online Instance Classifier Refinement](https://mlsanity.com/paper/1704.00138/) (2017)
- [Cross-domain Detection via Graph-induced Prototype Alignment](https://mlsanity.com/paper/2003.12849/) (2020)
- [Towards Universal Representation Learning for Deep Face Recognition](https://mlsanity.com/paper/2002.11841/) (2020)
- [Thoracic Disease Identification and Localization with Limited Supervision](https://mlsanity.com/paper/1711.06373/) (2017)
- [FocalMix: Semi-Supervised Learning for 3D Medical Image Detection](https://mlsanity.com/paper/2003.09108/) (2020)
- [Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation](https://mlsanity.com/paper/2007.10787/) (2020)
- [Deep Mining External Imperfect Data for Chest X-ray Disease Screening](https://mlsanity.com/paper/2006.03796/) (2020)
- [Deep Learning for Automatic Pneumonia Detection](https://mlsanity.com/paper/2005.13899/) (2020)
- [Knowledge Distillation with Adaptive Asymmetric Label Sharpening for Semi-supervised Fracture Detection in Chest X-rays](https://mlsanity.com/paper/2012.15359/) (2020)

## Built on by (most cited first, 5 of 5)

- [Deep Learning for Computational Cytology: A Survey](https://mlsanity.com/paper/2202.05126/) (2022)
- [Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays](https://mlsanity.com/paper/2206.03935/) (2022)
- [ORF-Net: Deep Omni-supervised Rib Fracture Detection from Chest CT Scans](https://mlsanity.com/paper/2207.01842/) (2022)
- [Scale Federated Learning for Label Set Mismatch in Medical Image Classification](https://mlsanity.com/paper/2304.06931/) (2023)
- [A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays](https://mlsanity.com/paper/2209.01988/) (2022)

## Abstract (arXiv)

Chest X-ray (CXR) is the most typical diagnostic X-ray examination for screening various thoracic diseases. Automatically localizing lesions from CXR is promising for alleviating radiologists' reading burden. However, CXR datasets are often with massive image-level annotations and scarce lesion-level annotations, and more often, without annotations. Thus far, unifying different supervision granularities to develop thoracic disease detection algorithms has not been comprehensively addressed. In this paper, we present OXnet, the first deep omni-supervised thoracic disease detection network to our best knowledge that uses as much available supervision as possible for CXR diagnosis. We first introduce supervised learning via a one-stage detection model. Then, we inject a global classification head to the detection model and propose dual attention alignment to guide the global gradient to the local detection branch, which enables learning lesion detection from image-level annotations. We also impose intra-class compactness and inter-class separability with global prototype alignment to further enhance the global information learning. Moreover, we leverage a soft focal loss to distill the soft pseudo-labels of unlabeled data generated by a teacher model. Extensive experiments on a large-scale chest X-ray dataset show the proposed OXnet outperforms competitive methods with significant margins. Further, we investigate omni-supervision under various annotation granularities and corroborate OXnet is a promising choice to mitigate the plight of annotation shortage for medical image diagnosis.