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Conference Papers Year : 2020

Real-Time Detection of Glomeruli in Renal Pathology

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Abstract

The field of digital pathology emerged with the introduction of whole slide imaging scanners and lead to the development of new tools for analyzing histopathological slides. The availability of digital representation of the slides has motivated the development of artificial intelligence methods to automatically identify microscopic structures in order to support pathologists in their diagnosis. Unlike many existing approaches targeting the detection of microscopic structures on static images at a given and fixed magnification level, our work focuses on the real-time detection of the structures at different scales. Indeed, real-time detection at different scales brings additional challenges but also better mimics the way pathologists work as they continuously move the slides and change the magnification level during their analysis. In this paper, we focus on renal pathology and more specifically on the real-time detection of glomeruli at different scales. Our method is based on the deep learning object detection model YOLOv3 pre-trained on the COCO dataset and fine tuned to detect glomeruli. We investigate the benefits of using multiscale images to improve the network ability to detect glomeruli at variable magnification levels in real time. Index Terms-real-time glomerulus detection, digital pathology, deep convolutional networks
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Dates and versions

hal-03353271 , version 1 (23-09-2021)

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Robin Heckenauer, Jonathan Weber, Cedric Wemmert, Friedrich Feuerhake, Michel Hassenforder, et al.. Real-Time Detection of Glomeruli in Renal Pathology. 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Jul 2020, Rochester (on line), United States. pp.350-355, ⟨10.1109/cbms49503.2020.00072⟩. ⟨hal-03353271⟩
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