140 citations · 156 across the 9 of their papers we have counts for
9 papers
Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
Dominik Müller, Philip Meyer, Lukas Rentschler +10
Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11…
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…
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,…
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks
Dominik Müller, Iñaki Soto-Rey, Frank Kramer
Novel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies. The idea of ensemble learning is to assemble diverse models or…
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…
Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models
Dominik Müller, Iñaki Soto-Rey, Frank Kramer
Preventable or undiagnosed visual impairment and blindness affect billion of people worldwide. Automated multi-disease detection models offer great potential to address this proble…
MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
Dominik Müller, Frank Kramer
The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not pr…