Unsupervised domain adaptation (UDA) involves learning class semantics from labeled data within a source domain that generalize to an unseen target domain. UDA methods are particularly impactful for ...
Abstract: Text-driven medical image segmentation aims to accurately segment pathological regions in medical images based on textual descriptions. Existing methods face two major challenges: (a) The ...
Abstract: Computed tomography (CT) is extensively used for accurate visualization and segmentation of organs and lesions. While deep learning models such as convolutional neural networks (CNNs) and ...
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