NDLI: Anatomy-specific classification of medical images using deep convolutional nets

Content Provider	IEEE Xplore Digital Library
Author	Roth, H.R. Lee, C.T. Hoo-Chang Shin Seff, A. Kim, L. Jianhua Yao Le Lu Summers, R.M.
Copyright Year	2015
Description	Author affiliation: Radiol. & Imaging Sci. Dept., Nat. Inst. of Health Clinical Center, Bethesda, MD, USA (Roth, H.R.; Lee, C.T.; Hoo-Chang Shin; Seff, A.; Kim, L.; Jianhua Yao; Le Lu; Summers, R.M.)
Abstract	Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the human body makes classification difficult. “Deep learning” methods such as convolutional networks (ConvNets) outperform other state-of-the-art methods in image classification tasks. In this work, we present a method for organ- or body-part-specific anatomical classification of medical images acquired using computed tomography (CT) with ConvNets. We train a ConvNet, using 4,298 separate axial 2D key-images to learn 5 anatomical classes. Key-images were mined from a hospital PACS archive, using a set of 1,675 patients. We show that a data augmentation approach can help to enrich the data set and improve classification performance. Using ConvNets and data augmentation, we achieve anatomy-specific classification error of 5.9 % and area-under-the-curve (AUC) values of an average of 0.998 in testing. We demonstrate that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis.
Starting Page	101
Ending Page	104
File Size	1603872
Page Count	4
File Format	PDF
ISBN	9781479923748
DOI	10.1109/ISBI.2015.7163826
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computed tomography Medical diagnostic imaging Convolution Lungs Training Neural networks Deep Learning Image Classification Computed tomography (CT) Convolutional Networks
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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