The process of creating an ML model by developers

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JoyuntoExpate
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Joined: Sat Feb 11, 2023 7:04 am

The process of creating an ML model by developers

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Repetition rate (percentage of women who are called for further examination) would be - , and the cancer detection rate would be . people per thousand patients. The Gleason score is a popular prostate cancer scoring system that was developed back in . Physicians have historically evaluated tissue biopsies visually. And only in , Martin Stump (from AI and Data Science) and Craig Mermel (from Google AI Healthcare) developed a deep learning system for the Gleason scale. Diagnostic accuracy was . (on a scale of . "random" to " correct"). This is an excellent result compared to the diagnosis made by doctors whose accuracy.

The image is loaded into a system that prioritizes a list of studies, from the most consumer email list likely pathology to the least. So the doctor will first look at the pictures of patients in whom the system predicted a neoplasm. Or the specialist looks at the image, where the AI ??has highlighted the area of ??pathology with a marker, and makes his remark in the description of the image made by the AI. consists of many stages - data collection and labeling, image preprocessing (for example, organ segmentation and removal of unnecessary parts of images), neural network training, and results calibration for a specific application scenario.

Image

One of the most difficult stages is segmentation (selection of features) for classification into a malignant or benign tumor. Different imaging and training processes are used to recognize and segment each type of cancer, which are different for diagnosing, for example, skin cancer and leukemia. Machine learning is being actively tested to detect brain, breast, lung, skin, blood and liver cancers. In Russia, in , the Center for Diagnostics and Telemedicine launched a prospective experiment to test AI solutions. A large number of images (obtained using CT, MRI, mammography or histopathology) are collected into datasets - datasets that have become used to train machine learning algorithms.
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