Abstract
Patients suffering with type 1 diabetes show a major reduction of β -cells within their pancreas. By analyzing tissue samples containing granules – the insulin-producing units within the β -cells – we aim to gather more information on the respective healthy and diabetic phenotypes, which could lead to further understanding the pathogenesis of the disease. To this end, we use a deep learning approach to investigate whether assumptions on the pathological status can be made based on electron micrograph images of β -cells. To support the decision- making process we explore whether estimating the number of granules can be used to aid in discriminating healthy from diabetic samples. Furthermore, we demonstrate that multi-task and transfer learning strategies can lead to more accurate predictions. Finally, this work intends to contribute to a more in-depth understanding of the structural mechanisms in type 1 diabetes, which is essential to design better approaches to a tailored treatment.
Originalsprache | englisch |
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Titel | Computer Vision and Pattern Analysis Across Domains |
Redakteure/-innen | Markus Seidl, Matthias Zeppelzauer, Peter M. Roth |
Herausgeber (Verlag) | Verlag der Technischen Universität Graz |
Seiten | 45-50 |
ISBN (elektronisch) | 978-3-85125-869-1 |
Publikationsstatus | Veröffentlicht - 2022 |
Veranstaltung | 44th OAGM Workshop 2021: Computer Vision and Pattern Analysis Across Domains: ÖAGM 2021 - University of Applied Sciences St. Pölten, abgesagt, Österreich Dauer: 24 Nov. 2021 → 25 Nov. 2021 |
Konferenz
Konferenz | 44th OAGM Workshop 2021: Computer Vision and Pattern Analysis Across Domains |
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Land/Gebiet | Österreich |
Ort | abgesagt |
Zeitraum | 24/11/21 → 25/11/21 |