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Title:Application of deep neural network to prostate cancer diagnosis
Author(s):Zhang, Feifan
Contributor(s):Popescu, Gabriel
Subject(s):Prostate Cancer
Semantic Segmentation
U-Net
Gleason Score
Abstract:Deep neural network (DNN) has been widely used in biomedical fields to help in understanding diseases. A well-trained DNN can learn inconspicuous features and give a better overall diagnosis than a human. Prostate cancer, one of the most dangerous cancers, has a 5-year survival rate of around 70% when diagnosed in Stage II but this rate decrements to 40% when diagnosed in Stage III. Clearly, we need to find a way help diagnose prostate cancer as early as possible. In order to build such a diagnosis network, we make use of the distinctive gland distribution in Prostate cells, training a U-Net (one kind of deep convolutional neural network) to do the cell semantic segmentation. This segmentation gives us a 3-label output image which labels the areas of gland, stroma and background in the input image. Based on the segmentation result, the U-net is trained to learn and output the prediction stage of prostate cancer data. This project uses Tensorflow library in Python to build and train the U-net.
Issue Date:2019-05
Genre:Other
Type:Text
Language:English
URI:http://hdl.handle.net/2142/104053
Date Available in IDEALS:2019-06-19


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