Background: Fundus vessel segmentation is crucial for the early diagnosis of ocular diseases. However, existing deep learning-based methods, although effective for detecting coarse vessels, still face ...
Background: This study aims to investigate the application of visual information processing mechanisms in the segmentation of stem cell (SC) images. The cognitive principles underlying visual ...
In this post, we will show you how to use MAI-Image-1 for HD image generation on a Windows PC. Microsoft has recently introduced its first text-to-image model built completely in-house. Known as ...
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 ...
A research team led by Prof. WANG Huanqin at the Institute of Intelligent Machines, the Hefei Institutes of Physical Science of the Chinese Academy of Sciences, recently proposed a semi-supervised ...
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 ...
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 ...
Scientists have created an AI tool that could help doctors identify diseases quickly and accurately using only a small number of medical images. Credit: Victoria Kotlyarchuk/iStock A new artificial ...
ABSTRACT: In this paper, a novel multilingual OCR (Optical Character Recognition) method for scanned papers is provided. Current open-source solutions, like Tesseract, offer extremely high accuracy ...
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