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Found 5 papers, 4 papers with code
Multi-Modal Dataset Creation for Federated Learning with DICOM Structured Reports
Methods: DICOM structured reports enable the standardized linkage of arbitrary information beyond the imaging domain and can be used within Python deep learning pipelines with highdicom.
Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging
First, CNNs predict on unlabeled data per label type and then the transformer learns from these predictions with label-specific heads.
mvHOTA: A multi-view higher order tracking accuracy metric to measure spatial and temporal associations in multi-point detection
The main evaluation metric available comes from Multi-object tracking (MOT) methods to benchmark performance on datasets such as KITTI with the recently proposed higher order tracking accuracy (HOTA) metric, which is capable of providing a better description of the performance over metrics such as MOTA, DetA, and IDF1.
Point detection through multi-instance deep heatmap regression for sutures in endoscopy
Method: In this work, we formulate the suture detection task as a multi-instance deep heatmap regression problem, to identify entry and exit points of sutures.
Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation
In this use case, it is of paramount importance to display objects like needles, sutures or instruments consistent in both domains while altering the style to a more tissue-like appearance.
