Devdiscourse

Self-supervised AI can unlock hidden disease patterns

Published as an arXiv preprint, the paper details how unsupervised and self-supervised AI models are matching or surpassing ...
IEEE

CiSeg: Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation via Causal Intervention

Abstract: Unsupervised domain adaptation (UDA) addresses the domain shift problem by transferring knowledge from labeled source domain data (e.g. CT) to unlabeled target domain data (e.g. MRI). While ...
EurekAlert!

Unsupervised cross-domain adaptation: single-pixel image transmission in unknown dynamic channels

Imaging technologies are ubiquitous in our daily lives, from smartphone cameras to medical imaging devices, helping us capture images and perceive objects. However, when faced with complex ...
IEEE

Unsupervised Brain Lesion Segmentation Using Posterior Distributions Learned by Subspace-based Generative Model

Abstract: Unsupervised brain lesion segmentation, focusing on learning normative distributions from images of healthy subjects, are less dependent on lesion-labeled data, thus exhibiting better ...
Scientific Research Publishing

From U-Net to Swin-Unet Transformers: The Next-Generation Advances in Brain Tumor Segmentation with Deep Learning ()

Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...