Titlebook: Data Engineering in Medical Imaging; Second MICCAI Worksh Binod Bhattarai,Sharib Ali,Danail Stoyanov Conference proceedings 2025 The Editor

情感

龍卷風

催眠

accrete

artless

,Cross-Task Data Augmentation by?Pseudo-Label Generation for?Region Based Coronary Artery Instance Sudo-labels generated on a dataset of separate related task to diversify and improve model performance. This method increases the baseline F1 score by 9% in the validation data set and by 3% in the test data set.

WAX

,Real Time Multi Organ Classification on?Computed Tomography Images,tes as an independent classifier at query locations, it can generate full segmentations by querying grid locations at any resolution, offering faster performance than segmentation algorithms. We compared our method with existing segmentation techniques, demonstrating its superior runtime potential for practical applications in medical imaging.

泄露

STENT

針葉類的樹

Mario Cardano,Marco Castagnettotes as an independent classifier at query locations, it can generate full segmentations by querying grid locations at any resolution, offering faster performance than segmentation algorithms. We compared our method with existing segmentation techniques, demonstrating its superior runtime potential for practical applications in medical imaging.

本能

https://doi.org/10.1007/978-3-031-73748-0data augmentation; synthetic data; active learning; medical imaging; data synthesis; federated learning; m