Search Results for author:
Found 3 papers, 1 papers with code
Screener: Self-supervised Pathology Segmentation Model for 3D Medical Images
Accurate segmentation of all pathological findings in 3D medical images remains a significant challenge, as supervised models are limited to detecting only the few pathology classes annotated in existing datasets.
Anatomical Positional Embeddings
We train our APE model on 8400 publicly available CT images of abdomen and chest regions.
The impact of deep learning aid on the workload and interpretation accuracy of radiologists on chest computed tomography: a cross-over reader study
Previous studies have measured the time-saving effect of using a deep-learning-based aid (DLA) for CT interpretation.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
