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Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation
To the best of our knowledge, this is the first work in which CBM, such as label fusion techniques, are used to solve a problem of collaborative learning.
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection
In addition, we show the efficacy of this approach on out-of-sample data showcasing the generalizability of this approach with limited training data.
Assessing the Performance of Automated Prediction and Ranking of Patient Age from Chest X-rays Against Clinicians
Understanding the internal physiological changes accompanying the aging process is an important aspect of medical image interpretation, with the expected changes acting as a baseline when reporting abnormal findings.
Learning to detect chest radiographs containing lung nodules using visual attention networks
We propose two network architectures for the classification of images likely to contain pulmonary nodules using both weak labels and manually-delineated bounding boxes, when these are available.
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