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Extending ”PathoLearn” with an End-To-End Artificial Intelligence Platform

  • Pathologists need to identify abnormal changes in tissue. With the developing digitalization, the used tissue slides are stored digitally. This enables pathologists to annotate the region of interest with the support of software tools. PathoLearn is a web-based learning platform explicitly developed for the teacher-student scenario, where the goal is that students learn to identify potential abnormal changes. Artificial intelligence (AI) and machine learning (ML) have become very important in medicine. Many health sectors already utilize AI and ML. This will only increase in the future, also in the field of pathology. Therefore, it is important to teach students the fundamentals and concepts of AI and ML early in their studies. Additionally, creating and training AI generally requires knowledge of programming and technical details. This thesis evaluates how this boundary can be overcome by comparing existing end-to-end AI platforms and teaching tools for AI. It was shown that a visual programming editor offers a fitting abstraction for creating neural networks without programming. This was extended with real-time collaboration to enable students to work in groups. Additionally, an automatic training feature was implemented, removing the necessity to know technical details about training neural networks.

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Author:Jannes Neemann
Advisor:Frauke SprengelORCiDGND, Nadine Sarah Schaadt
Document Type:Master's Thesis
Year of Completion:2023
Publishing Institution:Hochschule Hannover
Granting Institution:Hochschule Hannover, Fakultät IV - Wirtschaft und Informatik
Date of final exam:2023/10/26
Release Date:2024/02/26
Tag:E-Learning; Machine Learning; Pathology; Real-time Collaboration
GND Keyword:Pathologie; Lernsoftware; Maschinelles Lernen
Page Number:153
Institutes:Fakultät IV - Wirtschaft und Informatik
DDC classes:610 Medizin, Gesundheit
004 Informatik
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International