Annotating medical images, particularly for organ segmentation, is laborious and time-
consuming. For example, annotating an abdominal organ requires an estimated rate of 30 …

As a main branch of weakly supervised learning paradigm, partial label learning (PLL)
copes with the situation where each sample corresponds to ambiguous candidate labels …

Automatic multi-organ segmentation in medical images is crucial for many clinical
applications. The art methods have reported promising results but rely on massive …

Dental caries segmentation based on oral panoramic medical images is a demanding
medical task. However, it is challenging to determine imaging diagnosis results as the actual …

Due to the high human cost of annotation, it is non-trivial to curate a large-scale medical
dataset that is fully labeled for all classes of interest. Instead, it would be convenient to …

The importance of X-Ray imaging analysis is paramount for health care institutions since it is
the main imaging modality for patient diagnosis, and deep learning can be used to aid …

Right ventricular function has been associated with a variety of cardiovascular diseases. In
the clinical study of right ventricular function, an important step is segmentation of the right …

Optic disc (OD) and optic cup (OC) segmentation is an important task in ophthalmic
medicine and is crucial for aiding glaucoma screening. With the development of smart …

Medical image segmentation is fundamental in the field of medical image analysis and has
wide clinical applications in disease diagnosis and surgical planning etc. Current prevalent …

Using decentralized data for federated training is one promising emerging research
direction for alleviating data scarcity in the medical domain. However, in contrast to large …