Standardized Medical Image Classification across Medical Disciplines
This work addresses the need for adaptable tools in medical imaging research, though it is incremental as it applies an existing framework to new data without introducing novel methods.
The paper tackles the problem of standardizing medical image classification across diverse disciplines and imaging modalities by evaluating the AUCMEDI framework, achieving averaged AUC scores between 0.82 and 1.0 and F1 scores between 0.61 and 1.0 across multiple datasets.
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.