6 citations · 22 across the 8 of their papers we have counts for
8 papers
MISm: A Medical Image Segmentation Metric for Evaluation of weak labeled Data
Dennis Hartmann, Verena Schmid, Philip Meyer +3
Performance measures are an important tool for assessing and comparing different medical image segmentation algorithms. Unfortunately, the current measures have their weaknesses wh…
Standardized Medical Image Classification across Medical Disciplines
Simone Mayer, Dominik Müller, Frank Kramer
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 spe…
Towards a Guideline for Evaluation Metrics in Medical Image Segmentation
Dominik Müller, Iñaki Soto-Rey, Frank Kramer
In the last decade, research on artificial intelligence has seen rapid growth with deep learning models, especially in the field of medical image segmentation. Various studies demo…
Perspective on Code Submission and Automated Evaluation Platforms for University Teaching
Florian Auer, Johann Frei, Dominik Müller +1
We present a perspective on platforms for code submission and automated evaluation in the context of university teaching. Due to the COVID-19 pandemic, such platforms have become a…
MISeval: a Metric Library for Medical Image Segmentation Evaluation
Dominik Müller, Dennis Hartmann, Philip Meyer +3
Correct performance assessment is crucial for evaluating modern artificial intelligence algorithms in medicine like deep-learning based medical image segmentation models. However,…
Classification of Viral Pneumonia X-ray Images with the Aucmedi Framework
Pia Schneider, Dominik Müller, Frank Kramer
In this work we use the AUCMEDI-Framework to train a deep neural network to classify chest X-ray images as either normal or viral pneumonia. Stratified k-fold cross-validation with…