# Thoracic Disease Identification and Localization with Limited Supervision

arXiv: https://arxiv.org/abs/1711.06373 · HTML: https://mlsanity.com/paper/1711.06373/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2017.

## Built on by (most cited first, 30 of 55)

- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2203.00259/) (2022)
- [XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations](https://mlsanity.com/paper/2103.10663/) (2021)
- [Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification](https://mlsanity.com/paper/2210.12843/) (2022)
- [Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark](https://mlsanity.com/paper/2109.14956/) (2021)
- [A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation](https://mlsanity.com/paper/2104.10326/) (2021)
- [REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays](https://mlsanity.com/paper/2109.14187/) (2021)
- [Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis](https://mlsanity.com/paper/2112.12349/) (2021)
- [Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection](https://mlsanity.com/paper/2105.10160/) (2021)
- [Improving Pneumonia Localization via Cross-Attention on Medical Images and Reports](https://mlsanity.com/paper/2110.03094/) (2021)
- [Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays](https://mlsanity.com/paper/2206.12704/) (2022)
- [Label-Assemble: Leveraging Multiple Datasets with Partial Labels](https://mlsanity.com/paper/2109.12265/) (2021)
- [Visual Anomaly Detection Via Partition Memory Bank Module and Error Estimation](https://mlsanity.com/paper/2209.12441/) (2022)
- [Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack](https://mlsanity.com/paper/2103.03438/) (2021)
- [Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification](https://mlsanity.com/paper/2103.03629/) (2021)
- [SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection](https://mlsanity.com/paper/2406.00625/) (2024)
- [Probabilistic Integration of Object Level Annotations in Chest X-ray Classification](https://mlsanity.com/paper/2210.06980/) (2022)
- [I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR Diagnoses](https://mlsanity.com/paper/2309.13550/) (2023)
- [Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features](https://mlsanity.com/paper/2104.00411/) (2021)
- [Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models](https://mlsanity.com/paper/2104.02481/) (2021)
- [SCALP -- Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata](https://mlsanity.com/paper/2110.14787/) (2021)
- [Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction](https://mlsanity.com/paper/2105.14773/) (2021)
- [Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays](https://mlsanity.com/paper/2207.04394/) (2022)
- [A Prototype-Based Neural Network for Image Anomaly Detection and Localization](https://mlsanity.com/paper/2310.02576/) (2023)
- [A multi-modal vision-language model for generalizable annotation-free pathology localization](https://mlsanity.com/paper/2401.02044/) (2024)
- [Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop](https://mlsanity.com/paper/2104.04968/) (2021)
- [Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment](https://mlsanity.com/paper/2104.05261/) (2021)
- [Weakly Supervised Thoracic Disease Localization via Disease Masks](https://mlsanity.com/paper/2101.09915/) (2021)
- [OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays](https://mlsanity.com/paper/2104.03218/) (2021)
- [Hybrid Supervision Learning for Pathology Whole Slide Image Classification](https://mlsanity.com/paper/2107.00934/) (2021)

## Abstract (arXiv)

Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning. Building a highly accurate prediction model for these tasks usually requires a large number of images manually annotated with labels and finding sites of abnormalities. In reality, however, such annotated data are expensive to acquire, especially the ones with location annotations. We need methods that can work well with only a small amount of location annotations. To address this challenge, we present a unified approach that simultaneously performs disease identification and localization through the same underlying model for all images. We demonstrate that our approach can effectively leverage both class information as well as limited location annotation, and significantly outperforms the comparative reference baseline in both classification and localization tasks.