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Characterising Eye Movement Events With Multi-Scale Spatio-Temporal Awareness
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Contrastive Disentanglement for Quantitative Ultrasound Muscle Atrophy Evaluation
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DERE-Net: A dual-encoder residual enhanced U-Net for muscle fiber segmentation of H&E images
Accurate segmentation of hematoxylin-eosin (H&E) muscle fiber images is crucial for the diagnosis of weightless muscle atrophy. …
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Efficient Adaptive Incremental Learning for Fruit and Vegetable Classification
Traditional deep learning models for fruit and vegetable classification are usually implemented via training on an unchanged dataset. …
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Exploring Radiomics Features Based on H&E Images as Potential Biomarkers for Evaluating Muscle Atrophy: A Preliminary Study
Radiomics features have been widely used as novel biomarkers in the diagnosis of various diseases, but whether radiomics features …
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Feasibility study of functional near-infrared spectroscopy in the ventral visual pathway for real-life applications
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Multimodal contrastive learning for brain–machine fusion: From brain-in-the-loop modeling to brain-out-of-the-loop application
Harnessing the complementarity between the brain and artificial neural networks has shown great promise for the development of novel …
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Positive-unlabeled learning for coronary artery segmentation in CCTA images
Accurate three-dimensional (3D) segmentation of the coronary artery is an essential step in the quantitative analysis of the coronary …
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Relationship Between the Parietal Cortex and Task Switching: Transcranial Direct Current Stimulation Combined with an Event-related Potential Study
Task switching refers to a set of cognitive processes involved in shifting attention from one task to another. In recent years, …
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High-Resolution Swin Transformer for Automatic Medical Image Segmentation
The resolution of feature maps is a critical factor for accurate medical image segmentation. Most of the existing Transformer-based …
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