GitHub

A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation

This is the authors' PyTorch implementation of CUTS, MICCAI 2024. The official version is maintained in the Lab GitHub repo. Please be mindful that these datasets are relatively small in sample size.
Nature

Shifting to machine supervision: annotation-efficient semi and self-supervised learning for automatic medical image segmentation and classification

Medical imaging analysis plays a pivotal role in clinical decision-making, aiding in diagnosis, treatment planning, and monitoring. The advent of deep learning has significantly enhanced the ...
IEEE

Contrastive Registration for Unsupervised Medical Image Segmentation

Abstract: Medical image segmentation is an important task in medical imaging, as it serves as the first step for clinical diagnosis and treatment planning. While major success has been reported using ...
makeuseof

Supervised vs. Unsupervised Learning: What's the Difference?

Fawad is a Telecommunications Engineer and a cybersecurity writer at MUO. He has been writing on security and privacy topics since 2017 for publications like WizCase, vpnMentor, and many others. He ...
IEEE

Unsupervised Domain Adaptation for Medical Image Segmentation by Disentanglement Learning and Self-Training

Abstract: Unsupervised domain adaption (UDA), which aims to enhance the segmentation performance of deep models on unlabeled data, has recently drawn much attention. In this paper, we propose a novel ...
Frontiers

Evaluation of MRI Denoising Methods Using Unsupervised Learning

In this paper we evaluate two unsupervised approaches to denoise Magnetic Resonance Images (MRI) in the complex image space using the raw information that k-space holds. The first method is based on ...
Scientific Research Publishing

Ultrasonographic Segmentation of Fetal Lung with Deep Learning ()

1 Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China. 2 Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; ...
Nature

Unsupervised Medical Image Segmentation Based on the Local Center of Mass

Image segmentation is the process of partitioning the set of image pixels into subsets, where the pixels in each subset are related, e.g. with respect to their intensities and/or locations.