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

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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  • HAL Id : hal-02092926, version 1
  • OATAO : 22660

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Sonia Mejbri, Camille Franchet, Ismat Ara Reshma, Josiane Mothe, Pierre Brousset, et al.. Deep Analysis of CNN Settings for New Cancer whole-slide Histological Images Segmentation: the Case of Small Training Sets. 6th International conference on BioImaging (BIOIMAGING 2019), Feb 2019, Prague, Czech Republic. pp.120-128. ⟨hal-02092926⟩

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