TY - JOUR
AU - Summers, Ronald
AB - [ExploitingShin, Hoo-ChangandLu, LeeffectiveKim, LaurenlearningSeff, Ari on veryYao, Jianhua large-scale (>100K patients) Summers, Ronald medical image databases have been a major challenge in spite of noteworthy progress in computer vision. This chapter suggests an interleaved text/image deep learning system to extract and mineInterleaved Text/Image Mining the semantic interactions of radiologic images and reports, from a national research hospital’s Picture Archiving and Communication System. This chapter introduces a method to perform unsupervised learning (e.g., latent Dirichlet allocation, feedforward/recurrent neural net language models) on document- and sentence-level texts to generate semantic labels and supervised deep ConvNets with categorization and cross-entropy loss functions to map from images to label spaces. Keywords can be predicted for images in a retrieval manner, and presence/absence of some frequent types of disease can be predicted with probabilities. The large-scale datasets of extracted key images and their categorization, embedded vector labels, and sentence descriptions can be harnessed to alleviate deep learning’s “data-hungry” challenge in the medical domain.]
TI - Deep Learning and Convolutional Neural Networks for Medical Image Computing: Interleaved Text/Image Deep Mining on a Large-Scale Radiology Image Database
DA - 2017-07-14
UR - https://www.deepdyve.com/lp/springer-journals/deep-learning-and-convolutional-neural-networks-for-medical-image-cwWMHRc8cu
DP - DeepDyve
ER -