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Tag: Support Vector Machine

Osteosarcoma

Detection and quantification of bone cancer

Metastatic bone malignancies arise following prostate cancer (80% of cases, with 3% five-year survival rate), breast cancer (with no cure) or lung cancers (with 11% two-year survival rate). Bone metastases affect more than 400,000 people annually in the United States with frequent occurrences among patients undergoing irradiation and secondary effect to other treatments. Detection of skeletal metastases has a major impact on devising treatment strategies and prognosis. The solution offered by RSIP Vision produces 3D surfaces of bones and other skeletal-related structures to detect and quantify primary and metastatic bone cancer.

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Vessel Segmentation Using Deep Learning

Various segmentation methods, whether based on Convolution Neural Networks or traditional image processing techniques, can be used to delineate the vascular tree in clinical imaging. Given the few features distinguishing veins from arteries (usually brighter and thinner than veins), the challenge consists of training a binary classifier assigning each pixel to the category of vein or artery. This article covers the advantages of using CNNs and deep neural networks for the classification and segmentation of vessels in fundus images.

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Defining the Borders within Computer Vision

What’s the Difference between Computer Vision, Image Processing and Machine Learning?   In this page, you will learn about Machine Vision, Computer Vision and Image Processing. If you

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