PCA and K-means clustering applied to Raman and PL imaging reveal structural defects in silicon wafers, enhancing understanding of optoelectronic performance.
Published as an arXiv preprint, the paper details how unsupervised and self-supervised AI models are matching or surpassing ...
AI artist Refik Anadol uses massive datasets and AI to create immersive works shown around the world
Refik Anadol paints with what he calls "a thinking brush" The 40-year-old Turkish American is not an artist in the traditional sense; he uses a computer, massive amounts of data and artificial ...
Abstract: Hyperspectral imaging technology is considered a new paradigm for high-precision pathological image segmentation due to its ability to obtain spatial and spectral information of the detected ...
Crop segmentation, the process of identifying crop regions in images, is fundamental to agricultural monitoring tasks such as yield prediction, pest detection, and growth assessment. Traditional ...
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 ...
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 ...
Unsupervised Learning is often considered more challenging than supervised learning because there is no corresponding response variable for each observation. In this sense, we’re working blind and the ...
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