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Authors: Sonia Mejbri 1 ; Camille Franchet 2 ; Reshma Ismat-Ara 1 ; Josiane Mothe 1 ; Pierre Brousset 2 and Emmanuel Faure 1

Affiliations: 1 Toulouse Institute of Computer Science Research, Toulouse, France ; 2 The University Cancer Institute Toulouse, Oncopole, France

Keyword(s): Breast Cancer, Histological Image Analysis, Convolutional Neural Networks, Deep Learning, Semantic Segmentation.

Abstract: Accurate analysis and interpretation of stained biopsy images is a crucial step in the cancer diagnostic routine which is mainly done manually by expert pathologists. The recent progress of digital pathology gives us a challenging opportunity to automatically process these complex image data in order to retrieve essential information and to study tissue elements and structures. This paper addresses the task of tissue-level segmentation in intermediate resolution of histopathological breast cancer images. Firstly, we present a new medical dataset we developed which is composed of hematoxylin and eosin stained whole-slide images wherein all 7 tissues were labeled by hand and validated by expert pathologist. Then, with this unique dataset, we proposed an automatic end-to-end framework using deep neural network for tissue-level segmentation. Moreover, we provide a deep analysis of the framework settings that can be used in similar task by the scientific community.

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Paper citation in several formats:
Mejbri, S.; Franchet, C.; Ismat-Ara, R.; Mothe, J.; Brousset, P. and Faure, E. (2019). Deep Analysis of CNN Settings for New Cancer Whole-slide Histological Images Segmentation: The Case of Small Training Sets. In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - BIOIMAGING; ISBN 978-989-758-353-7; ISSN 2184-4305, SciTePress, pages 120-128. DOI: 10.5220/0007406601200128

@conference{bioimaging19,
author={Sonia Mejbri. and Camille Franchet. and Reshma Ismat{-}Ara. and Josiane Mothe. and Pierre Brousset. and Emmanuel Faure.},
title={Deep Analysis of CNN Settings for New Cancer Whole-slide Histological Images Segmentation: The Case of Small Training Sets},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - BIOIMAGING},
year={2019},
pages={120-128},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007406601200128},
isbn={978-989-758-353-7},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - BIOIMAGING
TI - Deep Analysis of CNN Settings for New Cancer Whole-slide Histological Images Segmentation: The Case of Small Training Sets
SN - 978-989-758-353-7
IS - 2184-4305
AU - Mejbri, S.
AU - Franchet, C.
AU - Ismat-Ara, R.
AU - Mothe, J.
AU - Brousset, P.
AU - Faure, E.
PY - 2019
SP - 120
EP - 128
DO - 10.5220/0007406601200128
PB - SciTePress

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