Frontiers

Advanced AI for missing modality learning and uncertainty‑aware medical image analysis

Modern medical imaging increasingly relies on artificial intelligence to support detection, diagnosis, and prognostic ...
The Next Web

Unconventional AI releases its first model, built on an oscillator architecture its founder says could cut power use by a factor of a thousand

Naveen Rao's Unconventional AI has released Un-0, an image generation model running on a simulated oscillator chip architecture it claims could slash power.
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.
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 ...
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; ...