MIT researchers developed an AI tool, MultiverSeg, to simplify the annotation of medical images for clinical research.

Xipu, University of Liverpool Joint Publication!"Can't see clearly at night? A systematic review breaks through the low-light vision dilemma" ...

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 ...

DHS shared the image after announcing an Indiana ICE detention center. IndyCar said they were not aware of the Department of Homeland Security (DHS) plan to release an AI-generated image of an IndyCar ...

A new artificial intelligence (AI) tool could make it much easier-and cheaper-for doctors and researchers to train medical imaging software, even when only a small number of patient scans are ...

1 College of Information Science and Engineering, Henan University of Technology, Zhengzhou, China 2 Institute for Complexity Science, Henan University of Technology, Zhengzhou, China Tongue is ...

Nathan Eddy works as an independent filmmaker and journalist based in Berlin, specializing in architecture, business technology and healthcare IT. He is a graduate of Northwestern University’s Medill ...

AI tools for image segmentation require large training datasets that are annotated with many examples of objects of interest, e.g. manual annotated cell nuclei for training a model for nuclear ...

Objective: Our research aims to develop an automated method for segmenting brain CT images in healthy 2-year-old children using the ResU-Net deep learning model. Building on this model, we aim to ...

Abstract: Unsupervised domain adaptation (UDA) for remote sensing image semantic segmentation aims to train a deep model on the labeled source domain and apply it to the unlabeled target domain.