NDLI: Transfer representation learning for medical image analysis

Content Provider	IEEE Xplore Digital Library
Author	Chuen-Kai Shie Chung-Hisang Chuang Chun-Nan Chou Meng-Hsi Wu Chang, E.Y.
Copyright Year	2015
Description	Author affiliation: HTC Corp., Taipei, Taiwan (Chuen-Kai Shie; Chung-Hisang Chuang; Chun-Nan Chou; Meng-Hsi Wu; Chang, E.Y.)
Abstract	There are two major challenges to overcome when developing a classifier to perform automatic disease diagnosis. First, the amount of labeled medical data is typically very limited, and a classifier cannot be effectively trained to attain high disease-detection accuracy. Second, medical domain knowledge is required to identify representative features in data for detecting a target disease. Most computer scientists and statisticians do not have such domain knowledge. In this work, we show that employing transfer learning can remedy both problems. We use Otitis Media (OM) to conduct our case study. Instead of using domain knowledge to extract features from labeled OM images, we construct features based on a dataset entirely OM-irrelevant. More specifically, we first learn a codebook in an unsupervised way from 15 million images collected from ImageNet. The codebook gives us what the encoders consider being the fundamental elements of those 15 million images. We then encode OM images using the codebook and obtain a weighting vector for each OM image. Using the resulting weighting vectors as the feature vectors of the OM images, we employ a traditional supervised learning algorithm to train an OM classifier. The achieved detection accuracy is 88.5% (89.63% in sensitivity and 86.9% in specificity), markedly higher than all previous attempts, which relied on domain experts to help extract features.
Starting Page	711
Ending Page	714
File Size	812960
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7318461
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Accuracy Training data Support vector machines Medical diagnostic imaging Yttrium
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Ensemble learning for the detection of facial dysmorphology

Automated down syndrome detection using facial photographs

Using support vector machines to detect medical fraud and abuse

Automatic retinal vessel classification using a Least Square-Support Vector Machine in VAMPIRE

Recursive channel selection techniques for brain computer interfaces

A preliminary study of moment-based texture analysis for medical images

Asynchronous brain-computer interfacing based on intended movement direction

Vision-based absence seizure detection

Feature extraction and classification for ultrasound images of lumbar spine with support vector machine

Transfer representation learning for medical image analysis

Similar Documents

Ensemble learning for the detection of facial dysmorphology

Automated down syndrome detection using facial photographs

Using support vector machines to detect medical fraud and abuse

Automatic retinal vessel classification using a Least Square-Support Vector Machine in VAMPIRE

Recursive channel selection techniques for brain computer interfaces

A preliminary study of moment-based texture analysis for medical images

Asynchronous brain-computer interfacing based on intended movement direction

Vision-based absence seizure detection

Feature extraction and classification for ultrasound images of lumbar spine with support vector machine

Transfer representation learning for medical image analysis