On the Influence of Beta Cell Granule Counting for Classification in Type 1 Diabetes

Lea Bogensperger, Marc Masana, Filip Ilic, Dagmar Kolb, Thomas R. Pieber, Thomas Pock

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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.
Original languageEnglish
Title of host publicationComputer Vision and Pattern Analysis Across Domains
EditorsMarkus Seidl, Matthias Zeppelzauer, Peter M. Roth
PublisherVerlag der Technischen Universität Graz
ISBN (Electronic)978-3-85125-869-1
Publication statusPublished - 2022
EventOAGM Workshop 2021: Computer Vision and Pattern Analysis Across Domains - University of Applied Sciences St. Pölten, abgesagt, Austria
Duration: 24 Nov 202125 Nov 2021


ConferenceOAGM Workshop 2021


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