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Titlebook: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperf; First MICCAI Worksho Qian Wang,Fausto

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21#
發(fā)表于 2025-3-25 06:50:44 | 只看該作者
CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT delay phase dynamic CT liver scans, filtering out anything else, including other types of liver contrast studies. To exploit as much training data as possible, we also introduce an aggregated cross entropy loss that can learn from scans only identified as “contrast”. Extensive experiments on a data
22#
發(fā)表于 2025-3-25 11:28:19 | 只看該作者
23#
發(fā)表于 2025-3-25 12:42:38 | 只看該作者
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and ImperfFirst MICCAI Worksho
24#
發(fā)表于 2025-3-25 16:16:56 | 只看該作者
25#
發(fā)表于 2025-3-25 20:45:46 | 只看該作者
Temporal Consistency Objectives Regularize the Learning of Disentangled Representations require explainability, whilst relying less on annotated data (since annotations can be tedious and costly). Here we build on recent innovations in style-content representations to learn anatomy, imaging characteristics (appearance) and temporal correlations. By introducing a self-supervised object
26#
發(fā)表于 2025-3-26 02:50:16 | 只看該作者
Multi-layer Domain Adaptation for Deep Convolutional Networksurthermore, the performance is not guaranteed on a sample from an unseen domain at test time, if the network was not exposed to similar samples from that domain at training time. This hinders the adoption of these techniques in clinical setting where the imaging data is scarce, and where the intra-
27#
發(fā)表于 2025-3-26 07:06:11 | 只看該作者
Intramodality Domain Adaptation Using Self Ensembling and Adversarial Trainingades drastically if the test data is from a domain different from training data. In this paper, we present and evaluate a novel unsupervised domain adaptation (DA) framework for semantic segmentation which uses self ensembling and adversarial training methods to effectively tackle domain shift betwe
28#
發(fā)表于 2025-3-26 10:34:21 | 只看該作者
29#
發(fā)表于 2025-3-26 14:00:34 | 只看該作者
Synthesising Images and Labels Between MR Sequence Types with CycleGANubjects unable to hold the breath or suffering from arrhythmia. RT image acquisitions during free breathing produce comparatively poor quality images, a trade-off necessary to achieve the high temporal resolution needed for RT imaging and hence are less suitable in the clinical assessment of cardiac
30#
發(fā)表于 2025-3-26 17:49:13 | 只看該作者
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