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caaxr2020

CAAXR database [annotated by radiologists]

In the CAAXR dataset, a total of 1,749 chest X-Rays from the publicly available BIMCV database have been annotated by six radiologists from Mahajan Labs (Mahajan Imaging, New Delhi, India). All the six radiologists had more than 5 years experience, with two among them having 10 and 15 years of experience of reading chest X-rays. All the X-rays were annotated only once by a single radiologist. X-rays with doubtful findings were discussed, and consensus resolution was obtained by the independent effort of the annotator.

Four evaluation protocols for CAAXR database are introduced. Three out of four protocols are designed for classification, and one for segmentation. All protocols use 5-fold cross-validation. The baseline results for semantic segmentation use three segmentation models- UNet, SegNet, and Mask-RCNN. The classification performance is evaluated using four deep learning models namely - DenseNet121, MobileNetv2, ResNet18, and VGG19. The models are evaluated using only train and validation splits, ensuring a fair estimate of the models’ performance on the testing data.

The protocol CSVs can be downloaded from here.

The annotations for the chest X-rays are provided here.

The trained baseline segmentation models can be downloaded from the link: Baseline models (10.3 GB)

 

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