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PREDICTING DCIS RECURRENCE RISK USING A MACHINE LEARNING-BASED HIGH-CONTENT IMAGE ANALYSIS APPROACH
PREDICTING DCIS RECURRENCE RISK USING A MACHINE LEARNING-BASED HIGH-CONTENT IMAGE ANALYSIS APPROACH
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机译：使用基于机器学习的高内涵图像分析方法预测DCIS发生风险
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摘要
Embodiments of the present systems and methods may provide improved capability to predict the risk of recurrence of ductal carcinoma in situ (DCIS) conditions using whole slide image analysis based on machine learning techniques. For example, in an embodiment, a computer-implemented method for determining treatment of a patient may comprise receiving an image of living tissue of a patient, annotating the entire image into tissue structures, extracting texture features from the annotated image, determining a distribution of the extracted texture features relative to tissue conditions, classifying the patient into a risk group based on the distribution, and treating the patient accordingly based on the risk group.
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