ChestX-Det10: Chest X-ray Dataset on Detection of Thoracic Abnormalities
This dataset addresses a bottleneck for researchers in medical imaging by enabling more precise disease localization, though it is incremental as it builds on existing classification and weakly supervised methods.
The authors tackled the lack of instance-level detection datasets for thoracic abnormalities in chest X-ray images by creating ChestX-Det10, a new benchmark with box-level annotations for 10 disease categories across approximately 3,500 images.
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.