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Authors: Jia Qu 1 ; Nobuyuki Hiruta 2 ; Kensuke Terai 2 ; Hirokazu Nosato 3 ; Masahiro Murakawa 4 and Hidenori Sakanashi 4

Affiliations: 1 Department of Intelligent Interaction Technologies, University of Tsukuba, Tsukuba and Japan ; 2 Department of Surgical Pathology, Toho University Sakura Medical Center, Sakura and Japan ; 3 Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST) and Japan ; 4 Department of Intelligent Interaction Technologies, University of Tsukuba, Tsukuba, Japan, Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST) and Japan

ISBN: 978-989-758-353-7

Keyword(s): Pathology Image, Deep Learning, Transfer Learning, Color-Index Local Auto-Correlation (CILAC).

Related Ontology Subjects/Areas/Topics: Bioimaging ; Biomedical Engineering ; Feature Recognition and Extraction Methods ; Histology and Tissue Imaging ; Medical Imaging and Diagnosis

Abstract: Deep learning using Convolutional Neural Networks (CNN) has been demonstrated unprecedentedly powerful for image classification. Subsequently, computer-aided diagnosis (CAD) for pathology image has been largely facilitated due to the deep learning related approaches. However, because of extremely high cost of pathologist's professional work, the lack of well annotated pathological image data to train deep neural networks is currently a big problem. Aiming at further improving the performance of deep neural networks and alleviating the lack of annotated pathology data, we propose a full-automatic knowledge transferring based stepwise fine-tuning scheme to make deep neural networks follow pathologist’s perception manner and understand pathology step by step. To realize this conception, we also introduce a new type of target correlation intermediate dataset which can be yielded by using fully automated processing. By extracting rough but stain-robust pathology-related information from un annotated pathology images with handcrafted features, and making use of these materials to intermediately train deep neural networks, deep neural networks are expected to acquire fundamental pathological knowledge in advance so that boosted in the final task. In experiments, we validate the new scheme on several well-known deep neural networks. Correspondingly, the results present solid evidence for the effectiveness and suggest feasibility for other tasks. (More)

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Paper citation in several formats:
Qu, J.; Hiruta, N.; Terai, K.; Nosato, H.; Murakawa, M. and Sakanashi, H. (2019). Enhanced Deep Learning for Pathology Image Classification: A Knowledge Transfer based Stepwise Fine-tuning Scheme.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2 BIOIMAGING: BIOIMAGING, ISBN 978-989-758-353-7, pages 92-99. DOI: 10.5220/0007356100920099

@conference{bioimaging19,
author={Jia Qu. and Nobuyuki Hiruta. and Kensuke Terai. and Hirokazu Nosato. and Masahiro Murakawa. and Hidenori Sakanashi.},
title={Enhanced Deep Learning for Pathology Image Classification: A Knowledge Transfer based Stepwise Fine-tuning Scheme},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2 BIOIMAGING: BIOIMAGING,},
year={2019},
pages={92-99},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007356100920099},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2 BIOIMAGING: BIOIMAGING,
TI - Enhanced Deep Learning for Pathology Image Classification: A Knowledge Transfer based Stepwise Fine-tuning Scheme
SN - 978-989-758-353-7
AU - Qu, J.
AU - Hiruta, N.
AU - Terai, K.
AU - Nosato, H.
AU - Murakawa, M.
AU - Sakanashi, H.
PY - 2019
SP - 92
EP - 99
DO - 10.5220/0007356100920099

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