Learning from crowds describes that the annotations of training data are obtained with
crowd-sourcing services. Multiple annotators each complete their own small part of the …

Learning semantic segmentation requires pixel-wise annotations, which can be time-
consuming and expensive. To reduce the annotation cost, we propose a superpixel-based …

Federated learning (FL) has emerged as a promising paradigm for training segmentation
models on decentralized medical data, owing to its privacy-preserving property. However …

Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic resonance
imaging (MRI) critically informs understanding of disease progression and helps to direct …

High-quality point cloud annotation is labor-intensive and time-consuming, but it serves as a
critical factor driving the success of LiDAR point cloud semantic segmentation. Leveraging …

The advent of clinical language models integrated into electronic health records (EHR) for
clinical decision support has marked a significant advancement, leveraging the depth of …

Training deep neural networks reliably requires access to large-scale datasets. However,
obtaining such datasets can be challenging, especially in the context of neuroimaging …

Current methods focusing on medical image segmentation suffer from incorrect annotations,
which is known as the noisy label issue. Most medical image segmentation with noisy labels …

The quality of the teacher label has a dramatic impact on the out-come of the machine
learning process. Medical image segmentation often suffers from a noisy ground truth (GT) …

Deep learning has demonstrated a capacity to revolutionise human life in the last several
years. Medical imaging, which has a vast data footprint, has emerged as a pioneering area …