دورية أكاديمية
Deep Learning for Semantic Segmentation vs. Classification in Computational Pathology: Application to Mitosis Analysis in Breast Cancer Grading
| العنوان: | Deep Learning for Semantic Segmentation vs. Classification in Computational Pathology: Application to Mitosis Analysis in Breast Cancer Grading |
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| المؤلفون: | Jiménez, Gabriel, Racoceanu, Daniel |
| المساهمون: | Pontificia Universidad Católica del Perú = Pontifical Catholic University of Peru (PUCP), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière AP-HP, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS) |
| المصدر: | ISSN: 2296-4185 ; Frontiers in Bioengineering and Biotechnology ; https://hal.sorbonne-universite.fr/hal-02182488Test ; Frontiers in Bioengineering and Biotechnology, 2019, 7, pp.145. ⟨10.3389/fbioe.2019.00145⟩. |
| بيانات النشر: | HAL CCSD Frontiers |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | deep learning, CNN, semantic segmentation, mitosis detection, mitosis segmentation, whole slide imaging, digital pathology, computational pathology, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SDV.MHEP.AHA]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], [SDV.CAN]Life Sciences [q-bio]/Cancer, [SDV.IB]Life Sciences [q-bio]/Bioengineering |
| الوصف: | International audience ; Existing computational approaches have not yet resulted in effective and efficient computer-aided tools that are used in pathologists' daily practice. Focusing on a computer-based qualification for breast cancer diagnosis, the present study proposes two deep learning architectures to efficiently and effectively detect and classify mitosis in a histopathological tissue sample. The first method consists of two parts, entailing a preprocessing of the digital histological image and a free-handcrafted-feature Convolutional Neural Network (CNN) used for binary classification. Results show that the methodology proposed can achieve 95% accuracy in testing, with an F1-score of 94.35%. This result is higher than the results using classical image processing techniques and also higher than the approaches combining CCNs with handcrafted features. The second approach is an end-to-end methodology using semantic segmentation. Results showed that this algorithm can achieve an accuracy higher than 95% in testing and an average Dice index of 0.6, higher than the existing results using CNNs (0.9 F1-score). Additionally, due to the semantic properties of the deep learning approach, an end-to-end deep learning framework is viable to perform both tasks: detection and classification of mitosis. The results show the potential of deep learning in the analysis of Whole Slide Images (WSI) and its integration to computer-aided systems. The extension of this work to whole slide images is also addressed in the last sections; as well as, some computational key points that are useful when constructing a computer-aided-system inspired by the proposed technology. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| العلاقة: | hal-02182488; https://hal.sorbonne-universite.fr/hal-02182488Test; https://hal.sorbonne-universite.fr/hal-02182488/documentTest; https://hal.sorbonne-universite.fr/hal-02182488/file/fbioe-07-00145.pdfTest |
| DOI: | 10.3389/fbioe.2019.00145 |
| الإتاحة: | https://doi.org/10.3389/fbioe.2019.00145Test https://hal.sorbonne-universite.fr/hal-02182488Test https://hal.sorbonne-universite.fr/hal-02182488/documentTest https://hal.sorbonne-universite.fr/hal-02182488/file/fbioe-07-00145.pdfTest |
| حقوق: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.11CF4E4C |
| قاعدة البيانات: | BASE |
| DOI: | 10.3389/fbioe.2019.00145 |
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