Standardized Medical Image Classification across Medical Disciplines

• URL: http://arxiv.org/abs/2210.11091v1
• Date: Thu, 20 Oct 2022 08:38:31 GMT
• Title: Standardized Medical Image Classification across Medical Disciplines
• Authors: Simone Mayer, Dominik M\"uller and Frank Kramer
• Abstract summary: AUCMEDI is a Python-based framework for medical image classification. In this paper, we evaluate the capabilities of AUCMEDI, by applying it to multiple datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AUCMEDI is a Python-based framework for medical image classification. In this paper, we evaluate the capabilities of AUCMEDI, by applying it to multiple datasets. Datasets were specifically chosen to cover a variety of medical disciplines and imaging modalities. We designed a simple pipeline using Jupyter notebooks and applied it to all datasets. Results show that AUCMEDI was able to train a model with accurate classification capabilities for each dataset: Averaged AUC per dataset range between 0.82 and 1.0, averaged F1 scores range between 0.61 and 1.0. With its high adaptability and strong performance, AUCMEDI proves to be a powerful instrument to build widely applicable neural networks. The notebooks serve as application examples for AUCMEDI.

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