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From cellular phenotypes to the analysis of whole slide images : Application to treatment response in triple-negative breast cancer

Abstract : The rise of digital pathology and with it the challenges of histopathology analysis have been the focus of a worldwide effort in the overall fight against cancer. In parallel, the recent success of automated decision-making, machine learning, and specifically deep learning, have revolutionised the basis of research as we know today. In this thesis, we tackle the prediction of treatment response in triple-negative breast cancer patients with two different approaches that reach similar outcomes. The first line of approach, based on the recent success of computer vision, extracts learned features from the data in order to perform classification. The second line of approach forces the information flow to pass through nuclei segmentation. In particular, it allows the incorporation of high-resolution information on to a lower resolution overview. Yet while this approach is more appealing as it is based on the analysis and quantification of a precise biological element, nuclei segmentation is troublesome. While solving the task of nuclei segmentation with deep learning, we propose a new formulation for nuclei segmentation which excels at separating touching objects.
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https://pastel.archives-ouvertes.fr/tel-02470754
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Submitted on : Friday, February 7, 2020 - 3:00:29 PM
Last modification on : Thursday, April 9, 2020 - 5:08:12 PM
Document(s) archivé(s) le : Friday, May 8, 2020 - 4:13:26 PM

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2019PSLEM051_archivage.pdf
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  • HAL Id : tel-02470754, version 1

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Peter Naylor. From cellular phenotypes to the analysis of whole slide images : Application to treatment response in triple-negative breast cancer. Bioinformatics [q-bio.QM]. PSL Research University, 2019. English. ⟨NNT : 2019PSLEM051⟩. ⟨tel-02470754⟩

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