# Standardized Medical Image Classification across Medical Disciplines

arXiv: https://arxiv.org/abs/2210.11091 · HTML: https://mlsanity.com/paper/2210.11091/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [BCN20000: Dermoscopic Lesions in the Wild](https://mlsanity.com/paper/1908.02288/) (2019)

## Built on by (most cited first, 3 of 3)

- [DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks](https://mlsanity.com/paper/2403.16678/) (2024)
- [Towards Automated COVID-19 Presence and Severity Classification](https://mlsanity.com/paper/2305.08660/) (2023)
- [Classifying Mitotic Figures in the MIDOG25 Challenge with Deep Ensemble Learning and Rule Based Refinement](https://mlsanity.com/paper/2508.20919/) (2025)

## Abstract (arXiv)

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.