Generalization capabilities of learning-based medical image segmentation across domains
are currently limited by the performance degradation caused by the domain shift, particularly …

Ultrasound based estimation of fetal biometry is extensively used to diagnose prenatal
abnormalities and to monitor fetal growth, for which accurate segmentation of the fetal …

Deep classifiers may encounter significant performance degradation when processing
unseen testing data from varying centers, vendors, and protocols. Ensuring the robustness …

Colorectal cancers may occur in colon region of human body because of late detection of
polyps. Therefore, colonoscopists often use colonoscopy device to view the entire colon in …

In the medical field, the limited availability of large-scale datasets and labor-intensive
annotation processes hinder the performance of deep models. Diffusion-based generative …

Convolutional neural networks (CNNs) often suffer from poor performance when tested on
target data that differs from the training (source) data distribution, particularly in medical …

Applying pre-trained medical segmentation models on out-of-domain images often yields
predictions of insufficient quality. Several strategies have been proposed to maintain model …

In medical image segmentation tasks, the performance of the trained segmentation model in
the unseen domain is affected by the domain shifting problem. Therefore, improving the …

In medical image segmentation tasks, the performance of the trained segmentation model in
the unseen domain is affected by the domain shifting prob-lem. Therefore, improving the …

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