Active convolutional neural networks for cancerous tissue recognition

Panagiotis Stanitsas, Anoop Cherian, Alexander Truskinovsky, Vassilios Morellas, Nikolaos Papanikolopoulos

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

Abstract

Deep neural networks typically require large amounts of annotated data to be trained effectively. However, in several scientific disciplines, including medical image analysis, generating such large annotated datasets requires specialized domain knowledge, and hence is usually very expensive. In this work, we present a novel application of active learning to data sample selection for training Convolutional Neural Networks (CNN) for Cancerous Tissue Recognition (CTR). Our main idea is to steer annotation efforts towards selecting the most informative samples for training the CNN. To quantify informativeness, we explore three choices based on discrete entropy, best-vs-second-best, and k-nearest neighbor agreement. Our results on three different types of cancer datasets consistently demonstrate that under limited annotated samples, our proposed training scheme converges faster than classical randomized stochastic gradient descent, while achieving the same (or sometimes superior) classification accuracy.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages1367-1371
Number of pages5
ISBN (Electronic)9781509021758
DOIs
StatePublished - Feb 20 2018
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: Sep 17 2017Sep 20 2017

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Other

Other24th IEEE International Conference on Image Processing, ICIP 2017
CountryChina
CityBeijing
Period9/17/179/20/17

Fingerprint

Tissue
Neural networks
Image analysis
Entropy
Problem-Based Learning
Deep neural networks

Keywords

  • Active learning
  • Cancer detection
  • Deep learning
  • Uncertainty sampling

Cite this

Stanitsas, P., Cherian, A., Truskinovsky, A., Morellas, V., & Papanikolopoulos, N. (2018). Active convolutional neural networks for cancerous tissue recognition. In 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings (pp. 1367-1371). (Proceedings - International Conference on Image Processing, ICIP; Vol. 2017-September). IEEE Computer Society. https://doi.org/10.1109/ICIP.2017.8296505

Active convolutional neural networks for cancerous tissue recognition. / Stanitsas, Panagiotis; Cherian, Anoop; Truskinovsky, Alexander; Morellas, Vassilios; Papanikolopoulos, Nikolaos.

2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. IEEE Computer Society, 2018. p. 1367-1371 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2017-September).

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

Stanitsas, P, Cherian, A, Truskinovsky, A, Morellas, V & Papanikolopoulos, N 2018, Active convolutional neural networks for cancerous tissue recognition. in 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. Proceedings - International Conference on Image Processing, ICIP, vol. 2017-September, IEEE Computer Society, pp. 1367-1371, 24th IEEE International Conference on Image Processing, ICIP 2017, Beijing, China, 9/17/17. https://doi.org/10.1109/ICIP.2017.8296505
Stanitsas P, Cherian A, Truskinovsky A, Morellas V, Papanikolopoulos N. Active convolutional neural networks for cancerous tissue recognition. In 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. IEEE Computer Society. 2018. p. 1367-1371. (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2017.8296505
Stanitsas, Panagiotis ; Cherian, Anoop ; Truskinovsky, Alexander ; Morellas, Vassilios ; Papanikolopoulos, Nikolaos. / Active convolutional neural networks for cancerous tissue recognition. 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. IEEE Computer Society, 2018. pp. 1367-1371 (Proceedings - International Conference on Image Processing, ICIP).
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