For ViT-B/16, in addition to the scratch and ImageNet pre-trained settings, we used BiomedCLIP17 weights pre-trained on PMC-15 M, which consists of 15 million biomedical image-text pairs. When using the CNN and ViT backbones initialized with the pre-trained weights, we primarily employed...
Image, Text MIMIC-CXR CheXpert 224,316 chest radiographs of 65,240 patients; focused on medical analysis. Image, Text CheXpert MIMIC-III Health-related data from over 40K patients; includes clinical notes and structured data. Text MIMIC-III IU-Xray 7,470 pairs of chest X-rays and correspondin...
ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports. Eur Radiol. 2024;34(5):2817-2825. doi:10.1007/s00330-023-10213-1PubMedGoogle ScholarCrossref 13. Egli A. ChatGPT, GPT-4, and other large language models: the next revolution for ...
The system employed algorithms from different domains (e.g. image processing, visual perception, endoscopy, radiology and autonomous navigation). It provided additional visual information on the middle ear structures and the surgical instrument with submillimetric precision, compatible for middle ear ...