# Deep Mining External Imperfect Data for Chest X-ray Disease Screening

arXiv: https://arxiv.org/abs/2006.03796 · HTML: https://mlsanity.com/paper/2006.03796/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2020.

## Built on by (most cited first, 27 of 27)

- [PromptMRG: Diagnosis-Driven Prompts for Medical Report Generation](https://mlsanity.com/paper/2308.12604/) (2023)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Automated Generation of Accurate & Fluent Medical X-ray Reports](https://mlsanity.com/paper/2108.12126/) (2021)
- [Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images](https://mlsanity.com/paper/2104.03225/) (2021)
- [Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images](https://mlsanity.com/paper/2110.01761/) (2021)
- [Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays](https://mlsanity.com/paper/2206.03935/) (2022)
- [Self-supervised deep convolutional neural network for chest X-ray classification](https://mlsanity.com/paper/2103.03055/) (2021)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [Translation Consistent Semi-supervised Segmentation for 3D Medical Images](https://mlsanity.com/paper/2203.14523/) (2022)
- [Image Quality-aware Diagnosis via Meta-knowledge Co-embedding](https://mlsanity.com/paper/2303.15038/) (2023)
- [Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-rays](https://mlsanity.com/paper/2106.05915/) (2021)
- [Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teacher](https://mlsanity.com/paper/2312.01099/) (2023)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays](https://mlsanity.com/paper/2104.03218/) (2021)
- [ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification](https://mlsanity.com/paper/2210.02998/) (2022)
- [Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study](https://mlsanity.com/paper/2104.10553/) (2021)
- [Scale Federated Learning for Label Set Mismatch in Medical Image Classification](https://mlsanity.com/paper/2304.06931/) (2023)
- [Learning to Generalize towards Unseen Domains via a Content-Aware Style Invariant Model for Disease Detection from Chest X-rays](https://mlsanity.com/paper/2302.13991/) (2023)
- [Medical Image Debiasing by Learning Adaptive Agreement from a Biased Council](https://mlsanity.com/paper/2401.11713/) (2024)
- [Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical Image](https://mlsanity.com/paper/2405.12872/) (2024)
- [Labeled-to-Unlabeled Distribution Alignment for Partially-Supervised Multi-Organ Medical Image Segmentation](https://mlsanity.com/paper/2409.03228/) (2024)
- [External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data](https://mlsanity.com/paper/2201.00942/) (2022)
- [A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images using Convolutional Neural Networks](https://mlsanity.com/paper/2201.09952/) (2022)
- [BSM loss: A superior way in modeling aleatory uncertainty of fine_grained classification](https://mlsanity.com/paper/2206.04479/) (2022)
- [Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images](https://mlsanity.com/paper/2210.04227/) (2022)
- [Mixed-order self-paced curriculum learning for universal lesion detection](https://mlsanity.com/paper/2302.04677/) (2023)
- [Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection](https://mlsanity.com/paper/2609.10988/) (2026)

## Abstract (arXiv)

Deep learning approaches have demonstrated remarkable progress in automatic Chest X-ray analysis. The data-driven feature of deep models requires training data to cover a large distribution. Therefore, it is substantial to integrate knowledge from multiple datasets, especially for medical images. However, learning a disease classification model with extra Chest X-ray (CXR) data is yet challenging. Recent researches have demonstrated that performance bottleneck exists in joint training on different CXR datasets, and few made efforts to address the obstacle. In this paper, we argue that incorporating an external CXR dataset leads to imperfect training data, which raises the challenges. Specifically, the imperfect data is in two folds: domain discrepancy, as the image appearances vary across datasets; and label discrepancy, as different datasets are partially labeled. To this end, we formulate the multi-label thoracic disease classification problem as weighted independent binary tasks according to the categories. For common categories shared across domains, we adopt task-specific adversarial training to alleviate the feature differences. For categories existing in a single dataset, we present uncertainty-aware temporal ensembling of model predictions to mine the information from the missing labels further. In this way, our framework simultaneously models and tackles the domain and label discrepancies, enabling superior knowledge mining ability. We conduct extensive experiments on three datasets with more than 360,000 Chest X-ray images. Our method outperforms other competing models and sets state-of-the-art performance on the official NIH test set with 0.8349 AUC, demonstrating its effectiveness of utilizing the external dataset to improve the internal classification.