# Weakly Supervised Thoracic Disease Localization via Disease Masks

arXiv: https://arxiv.org/abs/2101.09915 · HTML: https://mlsanity.com/paper/2101.09915/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 42 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [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)
- [Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization](https://mlsanity.com/paper/1704.04232/) (2017)
- [Adversarial Complementary Learning for Weakly Supervised Object Localization](https://mlsanity.com/paper/1804.06962/) (2018)
- [When Radiology Report Generation Meets Knowledge Graph](https://mlsanity.com/paper/2002.08277/) (2020)
- [TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays](https://mlsanity.com/paper/1801.04334/) (2018)
- [Clinically Accurate Chest X-Ray Report Generation](https://mlsanity.com/paper/1904.02633/) (2019)
- [Attention-based Dropout Layer for Weakly Supervised Object Localization](https://mlsanity.com/paper/1908.10028/) (2019)
- [From Captions to Visual Concepts and Back](https://mlsanity.com/paper/1411.4952/) (2014)
- [Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering](https://mlsanity.com/paper/1505.05612/) (2015)
- [Self-produced Guidance for Weakly-supervised Object Localization](https://mlsanity.com/paper/1807.08902/) (2018)
- [Thoracic Disease Identification and Localization with Limited Supervision](https://mlsanity.com/paper/1711.06373/) (2017)
- [Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling](https://mlsanity.com/paper/2005.14480/) (2020)
- [InfoMask: Masked Variational Latent Representation to Localize Chest Disease](https://mlsanity.com/paper/1903.11741/) (2019)
- [Discretely-constrained deep network for weakly supervised segmentation](https://mlsanity.com/paper/1908.05770/) (2019)
- [Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays](https://mlsanity.com/paper/1807.00958/) (2018)
- [TUNA-Net: Task-oriented UNsupervised Adversarial Network for Disease Recognition in Cross-Domain Chest X-rays](https://mlsanity.com/paper/1908.07926/) (2019)
- [Adversarial regression training for visualizing the progression of chronic obstructive pulmonary disease with chest x-rays](https://mlsanity.com/paper/1908.10468/) (2019)
- [Multi-Scale Gradual Integration CNN for False Positive Reduction in Pulmonary Nodule Detection](https://mlsanity.com/paper/1807.10581/) (2018)
- [Dual-attention Focused Module for Weakly Supervised Object Localization](https://mlsanity.com/paper/1909.04813/) (2019)

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

- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images](https://mlsanity.com/paper/2310.06143/) (2023)
- [Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation](https://mlsanity.com/paper/2311.15213/) (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)
- [Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax Classification](https://mlsanity.com/paper/2403.18871/) (2024)

## Abstract (arXiv)

To enable a deep learning-based system to be used in the medical domain as a computer-aided diagnosis system, it is essential to not only classify diseases but also present the locations of the diseases. However, collecting instance-level annotations for various thoracic diseases is expensive. Therefore, weakly supervised localization methods have been proposed that use only image-level annotation. While the previous methods presented the disease location as the most discriminative part for classification, this causes a deep network to localize wrong areas for indistinguishable X-ray images. To solve this issue, we propose a spatial attention method using disease masks that describe the areas where diseases mainly occur. We then apply the spatial attention to find the precise disease area by highlighting the highest probability of disease occurrence. Meanwhile, the various sizes, rotations and noise in chest X-ray images make generating the disease masks challenging. To reduce the variation among images, we employ an alignment module to transform an input X-ray image into a generalized image. Through extensive experiments on the NIH-Chest X-ray dataset with eight kinds of diseases, we show that the proposed method results in superior localization performances compared to state-of-the-art methods.