Interleaved Text/Image Deep Mining on a Large-Scale Radiology Image Database View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Hoo-Chang Shin , Le Lu , Lauren Kim , Ari Seff , Jianhua Yao , Ronald Summers

ABSTRACT

Exploitingandeffectivelearning on very large-scale (>100K patients) 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 mine 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. More... »

PAGES

305-321

Book

TITLE

Deep Learning and Convolutional Neural Networks for Medical Image Computing

ISBN

978-3-319-42998-4
978-3-319-42999-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-42999-1_17

DOI

http://dx.doi.org/10.1007/978-3-319-42999-1_17

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1090672415


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