UNet and its variants have been widely used in medical image segmentation. However,
these models, especially those based on Transformer architectures, pose challenges due to …

Multitask learning (MTL) aims to learn multiple tasks simultaneously while exploiting their
mutual relationships. By using shared resources to simultaneously calculate multiple …

Self-supervised pretraining has been observed to be effective at improving feature
representations for transfer learning, leveraging large amounts of unlabelled data. This …

Alzheimer's disease (AD) is a common irreversible neurodegenerative disease among
elderlies. Establishing relationships between brain networks and cognitive scores plays a …

To address the problem of medical image recognition, computer vision techniques like
convolutional neural networks (CNN) are frequently used. Recently, 3D CNN-based models …

In real-world clinical settings, traditional deep learning-based classification methods
struggle with diagnosing newly introduced disease types because they require samples …

While pre-training on object detection tasks, such as Common Objects in Contexts
(COCO)[1], could significantly boost the performance of cell segmentation, it still consumes …

Precise cerebrovascular segmentation in Time-of-Flight Magnetic Resonance Angiography
(TOF-MRA) data is crucial for computer-aided clinical diagnosis. The sparse distribution of …

Deep learning has significantly advanced automatic medical diagnostics and released the
occupation of human resources to reduce clinical pressure, yet the persistent challenge of …

Gliomas, a kind of brain tumor characterized by high mortality, present substantial diagnostic
challenges in low-and middle-income countries, particularly in Sub-Saharan Africa. This …