BAMF-AIMI-Annotations

Many of the collections in IDC have limited annotations due to the expense and effort required to create these manually. The increased capabilities of AI analysis of radiology images provides an opportunity to augment existing IDC collections with new annotation data. To further this goal, we trained several nnU-Net based models for a variety of radiology segmentation tasks from public datasets and used them to generate segmentations for IDC collections. To validate the models performance, roughly 10% of the predictions were manually reviewed and corrected by both a board certified radiologist and a medical student (non-expert). Additionally, this non-expert looked at all the ai predictions and rated them on a 5 point Likert scale . This record provides AI segmentations, manually corrected segmentations, and manual scores for the inspected IDC Collection images. Please see the BAMF-AIMI-Annotations wiki page to learn more about the images and to obtain any supporting metadata for this collection.