When training on a long-tailed dataset, conventional learning algorithms tend to exhibit a
bias towards classes with a larger sample size. Our investigation has revealed that this …

Medical image datasets are often imbalanced due to biases in data collection and limitations
in acquiring data for rare conditions. Addressing class imbalance is crucial for developing …

Digital pathology has revolutionized the field of neuropathology, enabling rapid and precise
analysis of tissue samples. As neuropathologists and neurosurgeons increasingly embrace …

Being a significant repository of Buddhist imagery, Thangka images are valuable historical
materials of Tibetan studies, which covers many domains such as Tibetan history, politics …

The accurate segmentation of surgical instruments in endoscopic robotic surgery is a critical
challenge due to the intricate and dynamic nature of the surgical environment. Existing …

Cell nuclei segmentation in histopathology images is critical in computer-aided diagnosis
and treatment planning. However, this task is challenging due to inherent heterogeneity in …

Self-supervised learning has been widely used in natural image classification, but it has
been less applied in skin lesion image classification. The difficulty of existing self-supervised …

Microscopy is an indispensable tool in scientific research, enabling the observation of
objects too minuscule for the naked eye. Its application spans diverse fields, including …

In recent decades, the progress in computer vision techniques, particularly those based on
deep learning, has significantly enhanced the field of digitalised medical image analysis …