A ConvNeXt ensemble achieves 84% balanced accuracy on MIDOG25 atypical mitotic figure classification, while a rule-based refinement module trades sensitivity for specificity.
Standardized Medical Image Classification across Medical Disciplines
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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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Classifying Mitotic Figures in the MIDOG25 Challenge with Deep Ensemble Learning and Rule Based Refinement
A ConvNeXt ensemble achieves 84% balanced accuracy on MIDOG25 atypical mitotic figure classification, while a rule-based refinement module trades sensitivity for specificity.