L-CNN: Exploiting labeling latency in a CNN learning framework

Muhammad Jamal Afridi, Arun Ross, Erik M. Shapiro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A supervised learning system requires labeled data during the training phase. Obtaining labels can be an expensive process, especially in medical imaging applications where a qualified expert may be needed to carefully analyze images and annotate them. This constrains the amount of labeled data available. This study explores the possibility of incorporating labeling behavior (viz., labeling latency) in a supervised convolutional neural network (CNN) framework in order to improve its performance in the presence of limited labeled data. The problem of 'spot' detection in MRI scans is considered in this work. In this two-class problem, (a) labeling behavior is available only during the training phase unlike traditional features that are available both during training and testing; and (b) the labeling behavior is associated with only one class (the positive samples) unlike other side information that is available for all classes. To address these issues, a new CNN architecture referred to as L-CNN is designed. The proposed method utilizes the labeling behavior of the expert to cluster the labeled data into multiple categories; a source CNN is then trained to distinguish between these categories. Next, a transfer learning paradigm is used where a target CNN is initialized using this source CNN and its weights updated with the limited labeled data that is available. Experimental results on an existing MRI database show that the proposed L-CNN performs better than a conventional CNN and, further, significantly outperforms the previous state-of-the-art, thereby establishing a new baseline for 'spot' detection in MRI.

LanguageEnglish (US)
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2156-2161
Number of pages6
ISBN (Electronic)9781509048472
DOIs
StatePublished - Apr 13 2017
Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
Duration: Dec 4 2016Dec 8 2016

Other

Other23rd International Conference on Pattern Recognition, ICPR 2016
CountryMexico
CityCancun
Period12/4/1612/8/16

Profile

Labeling
Neural networks
Magnetic resonance imaging
Supervised learning
Medical imaging
Network architecture
Learning systems
Labels
Testing

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Afridi, M. J., Ross, A., & Shapiro, E. M. (2017). L-CNN: Exploiting labeling latency in a CNN learning framework. In 2016 23rd International Conference on Pattern Recognition, ICPR 2016 (pp. 2156-2161). [7899955] Institute of Electrical and Electronics Engineers Inc.. DOI: 10.1109/ICPR.2016.7899955

L-CNN : Exploiting labeling latency in a CNN learning framework. / Afridi, Muhammad Jamal; Ross, Arun; Shapiro, Erik M.

2016 23rd International Conference on Pattern Recognition, ICPR 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2156-2161 7899955.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Afridi, MJ, Ross, A & Shapiro, EM 2017, L-CNN: Exploiting labeling latency in a CNN learning framework. in 2016 23rd International Conference on Pattern Recognition, ICPR 2016., 7899955, Institute of Electrical and Electronics Engineers Inc., pp. 2156-2161, 23rd International Conference on Pattern Recognition, ICPR 2016, Cancun, Mexico, 12/4/16. DOI: 10.1109/ICPR.2016.7899955
Afridi MJ, Ross A, Shapiro EM. L-CNN: Exploiting labeling latency in a CNN learning framework. In 2016 23rd International Conference on Pattern Recognition, ICPR 2016. Institute of Electrical and Electronics Engineers Inc.2017. p. 2156-2161. 7899955. Available from, DOI: 10.1109/ICPR.2016.7899955
Afridi, Muhammad Jamal ; Ross, Arun ; Shapiro, Erik M./ L-CNN : Exploiting labeling latency in a CNN learning framework. 2016 23rd International Conference on Pattern Recognition, ICPR 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 2156-2161
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