人工智能
自编码
计算机科学
前列腺癌
模式识别(心理学)
特征(语言学)
深度学习
癌症检测
医学影像学
无监督学习
卷积神经网络
计算机视觉
癌症
医学
语言学
哲学
内科学
作者
Eldad Rubinstein,Moshe Salhov,Meital Nidam-Leshem,Valerie A. White,Shay Golan,Jack Baniel,Hanna Bernstine,David Groshar,Amir Averbuch
标识
DOI:10.1016/j.media.2019.04.001
摘要
Early detection and localization of prostate tumors pose a challenge to the medical community. Several imaging techniques, including PET, have shown some success. But no robust and accurate solution has yet been reached. This work aims to detect prostate cancer foci in Dynamic PET images using an unsupervised learning approach. The proposed method extracts three feature classes from 4D imaging data that include statistical, kinetic biological and deep features that are learned by a deep stacked convolutional autoencoder. Anomalies, which are classified as tumors, are detected in feature space using density estimation. The proposed algorithm generates promising results for sufficiently large cancer foci in real PET scans imaging where the foci is not viewed by the tomographic devices used for detection.
科研通智能强力驱动
Strongly Powered by AbleSci AI