Video capsule endoscopy has transformed gastrointestinal endoscopy (GIE) diagnostics by offering a non-invasive method for capturing detailed images of the gastrointestinal tract, enabling early disease detection. However, its potential is limited by the sheer volume of images generated during the imaging procedure, which can take anywhere from 6-8 hours and often produce up to 1 million images, necessitating automated analysis. Additionally, the variability of these images, combined with the need for expert annotations and the scarcity of large, high-quality labeled datasets, constrains the effectiveness of current medical image analysis models. To address this, we introduce a novel large gastrointestinal endoscopy dataset, called EndoExtend24, created by merging and re-stratifying the train/test splits of ten existing public and private datasets, ensuring no overlap of patient data across splits. EndoExtend24 includes over 226,000 labeled images, as well as dynamic class mappings, which allow unified training across datasets with differing labeling granularity, supporting up to 123 distinct pathological findings. Further, we propose to leverage domain adaptive pre-training of foundation models in computer vision trained with self-supervision on generic image data, to adapt them to the task of GIE medical diagnosis. Specifically, the EVA-02 model, which is based on the vision transformer architecture and was trained on ImageNet-22k with masked image modeling (using EVA-CLIP as a MIM teacher), is pre-trained on the novel EndoExtend24 dataset to achieve domain adaptation, and finally trained on the Capsule Endoscopy 2024 Challenge dataset. Experimental results demonstrate strong performance with an F1 score of 0.88, an improvement of about 39% over the baseline model's F1 score of 0.49. Additionally, the model achieved a macro AUC score of 0.993 and a balanced accuracy of 89.3%.
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