# ChestX-Det10: Chest X-ray Dataset on Detection of Thoracic Abnormalities

arXiv: https://arxiv.org/abs/2006.10550 · HTML: https://mlsanity.com/paper/2006.10550/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 30 of 38)

- [Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images](https://mlsanity.com/paper/2302.14042/) (2023)
- [CARZero: Cross-Attention Alignment for Radiology Zero-Shot Classification](https://mlsanity.com/paper/2402.17417/) (2024)
- [AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models](https://mlsanity.com/paper/2404.16233/) (2024)
- [Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling](https://mlsanity.com/paper/2306.03934/) (2023)
- [Medical Vision Language Pretraining: A survey](https://mlsanity.com/paper/2312.06224/) (2023)
- [UniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification](https://mlsanity.com/paper/2312.11038/) (2023)
- [Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark](https://mlsanity.com/paper/2207.11169/) (2022)
- [Large-scale Long-tailed Disease Diagnosis on Radiology Images](https://mlsanity.com/paper/2312.16151/) (2023)
- [M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation](https://mlsanity.com/paper/2408.16213/) (2024)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [A Systematic Review of Deep Learning-based Research on Radiology Report Generation](https://mlsanity.com/paper/2311.14199/) (2023)
- [Self-supervised visual learning in the low-data regime: a comparative evaluation](https://mlsanity.com/paper/2404.17202/) (2024)
- [A multi-modal vision-language model for generalizable annotation-free pathology localization](https://mlsanity.com/paper/2401.02044/) (2024)
- [Anatomy-guided Pathology Segmentation](https://mlsanity.com/paper/2407.05844/) (2024)
- [Low-Rank Knowledge Decomposition for Medical Foundation Models](https://mlsanity.com/paper/2404.17184/) (2024)
- [DWARF: Disease-weighted network for attention map refinement](https://mlsanity.com/paper/2406.17032/) (2024)
- [LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models](https://mlsanity.com/paper/2409.19540/) (2024)
- [RadZero: Similarity-Based Cross-Attention for Explainable Vision-Language Alignment in Chest X-ray with Zero-Shot Multi-Task Capability](https://mlsanity.com/paper/2504.07416/) (2025)
- [Walking the Tightrope: Disentangling Beneficial and Detrimental Drifts in Non-Stationary Custom-Tuning](https://mlsanity.com/paper/2505.13081/) (2025)
- [XBench: A Comprehensive Benchmark for Visual-Language Explanations in Chest Radiography](https://mlsanity.com/paper/2510.19599/) (2025)
- [ODverse33: Is the New YOLO Version Always Better? A Multi Domain benchmark from YOLO v5 to v11](https://mlsanity.com/paper/2502.14314/) (2025)
- [MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging](https://mlsanity.com/paper/2604.13756/) (2026)
- [Towards Robust Endogenous Reasoning: Unifying Drift Adaptation in Non-Stationary Tuning](https://mlsanity.com/paper/2604.15705/) (2026)
- [GLINT: Sparsely Gated Vision-Language Alignment for Fine-Grained Radiology Representations](https://mlsanity.com/paper/2606.03180/) (2026)
- [ACE: Anatomically Consistent Embeddings in Composition and Decomposition](https://mlsanity.com/paper/2501.10131/) (2025)
- [RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosis](https://mlsanity.com/paper/2509.19980/) (2025)
- [Hybrid Explanation-Guided Learning for Transformer-Based Chest X-Ray Diagnosis](https://mlsanity.com/paper/2510.12704/) (2025)
- [Concept-Guided Noisy Negative Suppression for Zero-Shot Classification and Grounding of Chest X-Ray Findings](https://mlsanity.com/paper/2605.19374/) (2026)
- [Frozen Foundation-Model Embeddings Discard Small-Lesion Signal in Chest Radiography: Implications for Pre-Deployment Evaluation](https://mlsanity.com/paper/2606.11606/) (2026)
- [Enhancing Generative Networks for Chest Anomaly Localization through Automatic Registration-Based Unpaired-to-Pseudo-Paired Training Data Translation](https://mlsanity.com/paper/2207.10324/) (2022)

## Abstract (arXiv)

Instance level detection of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images. Most existing works on chest X-rays focus on disease classification and weakly supervised localization. In order to push forward the research on disease classification and localization on chest X-rays. We provide a new benchmark called ChestX-Det10, including box-level annotations of 10 categories of disease/abnormality of ~ 3,500 images. The annotations are located at https://github.com/Deepwise-AILab/ChestX-Det10-Dataset.