140 citations · 174 across the 14 of their papers we have counts for
15 papers
Towards Automated COVID-19 Presence and Severity Classification
Dominik Müller, Niklas Schröter, Silvan Mertes +6
COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times. Especially for capacity planning of…
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…
Annotated Dataset Creation through General Purpose Language Models for non-English Medical NLP
Johann Frei, Frank Kramer
Obtaining text datasets with semantic annotations is an effortful process, yet crucial for supervised training in natural language processsing (NLP). In general, developing and app…
GERNERMED++: Transfer Learning in German Medical NLP
Johann Frei, Ludwig Frei-Stuber, Frank Kramer
We present a statistical model for German medical natural language processing trained for named entity recognition (NER) as an open, publicly available model. The work serves as a…
Nucleus Segmentation and Analysis in Breast Cancer with the MIScnn Framework
Adrian Pfleiderer, Dominik Müller, Frank Kramer
The NuCLS dataset contains over 220.000 annotations of cell nuclei in breast cancers. We show how to use these data to create a multi-rater model with the MIScnn Framework to autom…
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,…
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…
Assessing the Role of Random Forests in Medical Image Segmentation
Dennis Hartmann, Dominik Müller, Iñaki Soto-Rey +1
Neural networks represent a field of research that can quickly achieve very good results in the field of medical image segmentation using a GPU. A possible way to achieve good resu…
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…
Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net
Dominik Müller, Iñaki Soto Rey, Frank Kramer
The coronavirus disease 2019 (COVID-19) affects billions of lives around the world and has a significant impact on public healthcare. Due to rising skepticism towards the sensitivi…
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…