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

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arxiv 2006.10550 v3 pith:AZV3HTKN submitted 2020-06-17 eess.IV cs.CV

Jingyu Liu , Jie Lian , Yizhou Yu This is my paper · ORCID
classification eess.IVcs.CV
keywords chestdiseaseabnormalitiesannotationschestx-det10classificationdetectionimages
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

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 $\sim$ 3,500 images. The annotations are located at https://github.com/Deepwise-AILab/ChestX-Det10-Dataset.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Generative Foundation Model for Chest Radiography

    cs.CV 2025-09 conditional novelty 6.0 of 10

    A diffusion-based generative model for chest X-rays, trained on 960k image-report pairs, improves downstream classification, segmentation, detection, and fairness when its outputs are used for data augmentation or pre...

  2. SegX: Improving Interpretability of Clinical Image Diagnosis with Segmentation-based Enhancement

    eess.IV 2025-02 reject novelty 4.0 of 10

    SegX masks saliency maps with a clinical segmentation mask, and SegU uses the resulting overlap as a certainty score; experiments show modest overlap gains and weak uncertainty discrimination.

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