5 accepted papers
Predicting epileptic seizures effectively allows patients to take preventive measures in advance, reducing accident risk and enhancing safety. Several modeling challenges remain open: (1) The complex spatiotemporal dependency of EEG signals makes it challenging to design a model that efficiently ext…
Medical imaging is now a widely used test for the preoperative evaluation of focal cortical dysplasia (FCD). Deep learning-based methods can learn lesion features from image data to automatically recognize and segment FCD in epilepsy treatment. However, the existing FCD segmentation networks lack th…
The success of supervised deep learning heavily depends on large labeled datasets whose construction is often challenging in medical image analysis. Contrastive learning, a variant of self-supervised learning, is a potential solution to alleviate the strong demand for data annotation. In this work,…
Multiple Sclerosis (MS) lesion segmentation from MR images is important for neuroimaging analysis. MS is diffuse, multifocal, and tend to involve peripheral brain structures such as the white matter, corpus callosum, and brainstem. Recently, U-Net has made great achievements in medical image segment…
Hypertension is one of the most important contributors to heart disease and stroke. Multimodality medical image fusion plays an important role in the precise diagnosis, treatment planning and follow-up studies of various diseases. In this paper, we propose an image fusion framework in patients with…