Although the cerebellum is typically associated with supervised learning algorithms, it also exhibits extensive involvement in reward processing. In this study, we investigated the cerebellum's role in executing reinforcement learning algorithms, wit...
Self-supervised learning (SSL) has been proposed to alleviate neural networks' reliance on annotated data and to improve downstream tasks' performance, which has obtained substantial success in several volumetric medical image segmentation tasks. How...
Label scarcity, class imbalance and data uncertainty are three primary challenges that are commonly encountered in the semi-supervised medical image segmentation. In this work, we focus on the data uncertainty issue that is overlooked by previous lit...
Federated learning (FL) effectively mitigates the data silo challenge brought about by policies and privacy concerns, implicitly harnessing more data for deep model training. However, traditional centralized FL models grapple with diverse multi-cente...
OBJECTIVE: Persona validation is a challenging task, often relying on costly external validation methods. The aim of this study was the development of a novel method for Personas validation based on data already available during their creation.
Histopathological image classification using deep learning is crucial for accurate and efficient cancer diagnosis. However, annotating a large amount of histopathological images for training is costly and time-consuming, leading to a scarcity of avai...
. Metal artifacts severely damaged human tissue information from the computed tomography (CT) image, posing significant challenges to disease diagnosis. Deep learning has been widely explored for the metal artifact reduction (MAR) task. Nevertheless,...
Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole slide images (WSIs). We train an SSL Barlow Twins encoder on 435 colon adenocarcinoma WSIs from The C...
IEEE journal of biomedical and health informatics
Mar 6, 2025
Despite the similar global structures in Chest X-ray (CXR) images, the same anatomy exhibits varying appearances across images, including differences in local textures, shapes, colors, etc. Learning consistent representations for anatomical semantics...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 5, 2025
Medical image segmentation is important for quantitative disease diagnosis and treatment but relies on accurate pixel-wise labels, which are costly, time-consuming, and require domain expertise. This work introduces MIST (MIxed supervision, Self, and...
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