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).
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(Document in English)
PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 294kB |
Official URL: https://doi.org/10.5220/0007406601200128
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.
Item Type: | Conference or Workshop Item (Poster) |
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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: | |
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Deposited On: | 02 Apr 2019 13:17 |
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