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Deep Analysis of CNN Settings for New Cancer whole-slide Histological Images Segmentation: the Case of Small Training Sets

Mejbri, Sonia and Franchet, Camille and Reshma, Ismat Ara and Mothe, Josiane and Brousset, Pierre and Faure, Emmanuel Deep Analysis of CNN Settings for New Cancer whole-slide Histological Images Segmentation: the Case of Small Training Sets. (2018) In: 6th International conference on BioImaging (BIOIMAGING 2019), 22 February 2019 - 24 February 2019 (Prague, Czech Republic).

(Document in English)

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Official URL: https://doi.org/10.5220/0007406601200128


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.

Item Type:Conference or Workshop Item (Poster)
Additional Information:Thanks to SCITEPRESS (Science and Technology Publications) editor. The definitive version is available at http://www.scitepress.org This papers appears in Volume 2 Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies ISBN 978-989-758-353-7 The original PDF is available at: http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0007406601200128
HAL Id:hal-02092926
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Institut Claudius Regaud - ICR (FRANCE)
Other partners > Centre Hospitalier Universitaire de Toulouse - CHU Toulouse (FRANCE)
Other partners > Université de Montpellier (FRANCE)
Laboratory name:
Deposited On:02 Apr 2019 13:17

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