Imperial College London
Alternate

Professor Anil Anthony Bharath

Faculty of Engineering, Department of Bioengineering

Academic Director (Singapore)
 
 
 
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Contact

 

+44 (0)20 7594 5463a.bharath Website

 
 
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Location

 

4.12Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Creswell:2018:10.1049/iet-cvi.2018.5243,
author = {Creswell, A and Pouplin, A and Bharath, AA},
doi = {10.1049/iet-cvi.2018.5243},
journal = {IET Computer Vision},
title = {Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data.},
url = {http://dx.doi.org/10.1049/iet-cvi.2018.5243},
volume = {abs/1801.00693},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The authors propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but the amount of labelled data is limited. They consider the specific case of classifying skin lesions as either benign or malignant. In this setting, the authors’ proposed approach – the semi-supervised, denoising adversarial autoencoder – is able to utilise vast amounts of unlabelled data to learn a representation for skin lesions, and small amounts of labelled data to assign class labels based on the learned representation. They perform an ablation study to analyse the contributions of both the adversarial and denoising components and compare their work with state-of-the-art results. They find that their model yields superior classification performance, especially when evaluating their model at high sensitivity values.
AU  - Creswell,A
AU  - Pouplin,A
AU  - Bharath,AA
DO  - 10.1049/iet-cvi.2018.5243
PY  - 2018///
SN  - 1751-9640
TI  - Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data.
T2  - IET Computer Vision
UR  - http://dx.doi.org/10.1049/iet-cvi.2018.5243
VL  - abs/1801.00693
ER  -
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