no code implementations • 25 Mar 2024 • Dominik Müller, Philip Meyer, Lukas Rentschler, Robin Manz, Daniel Hieber, Jonas Bäcker, Samantha Cramer, Christoph Wengenmayr, Bruno Märkl, Ralf Huss, Frank Kramer, Iñaki Soto-Rey, Johannes Raffler
Overall, this study lays the groundwork for enhanced Gleason grading systems, potentially improving diagnostic efficiency for prostate cancer.
no code implementations • 15 May 2023 • Dominik Müller, Niklas Schröter, Silvan Mertes, Fabio Hellmann, Miriam Elia, Wolfgang Reif, Bernhard Bauer, Elisabeth André, Frank Kramer
COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times.
1 code implementation • 24 Oct 2022 • Dennis Hartmann, Verena Schmid, Philip Meyer, Iñaki Soto-Rey, Dominik Müller, Frank Kramer
Performance measures are an important tool for assessing and comparing different medical image segmentation algorithms.
1 code implementation • 20 Oct 2022 • Simone Mayer, Dominik Müller, Frank Kramer
AUCMEDI is a Python-based framework for medical image classification.
1 code implementation • 30 Aug 2022 • Johann Frei, Frank Kramer
Obtaining text datasets with semantic annotations is an effortful process, yet crucial for supervised training in natural language processsing (NLP).
1 code implementation • 29 Jun 2022 • 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.
2 code implementations • 16 Jun 2022 • Adrian Pfleiderer, Dominik Müller, Frank Kramer
The NuCLS dataset contains over 220. 000 annotations of cell nuclei in breast cancers.
1 code implementation • 10 Feb 2022 • 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.
1 code implementation • 27 Jan 2022 • Dominik Müller, Iñaki Soto-Rey, Frank Kramer
However, it is still an open question to what extent as well as which ensemble learning strategies are beneficial in deep learning based medical image classification pipelines.
1 code implementation • 23 Jan 2022 • Dominik Müller, Dennis Hartmann, Philip Meyer, Florian Auer, Iñaki Soto-Rey, Frank Kramer
Thus, we propose our open-source publicly available Python package MISeval: a metric library for Medical Image Segmentation Evaluation.
no code implementations • 3 Oct 2021 • Pia Schneider, Dominik Müller, Frank Kramer
Evaluation metrics (Classification-Report, macro f1-scores, Confusion-Matrices, ROC-Curves) of the individual folds and the ensembles show that the classifier works well.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
1 code implementation • 24 Sep 2021 • Johann Frei, Frank Kramer
The current state of adoption of well-structured electronic health records and integration of digital methods for storing medical patient data in structured formats can often considered as inferior compared to the use of traditional, unstructured text based patient data documentation.
no code implementations • 30 Mar 2021 • Dennis Hartmann, Dominik Müller, Iñaki Soto-Rey, Frank Kramer
Our results indicate that random forest approaches are a good alternative to deep convolutional neural networks and, thus, allow the usage of medical image segmentation without a GPU.
2 code implementations • 26 Mar 2021 • Dominik Müller, Iñaki Soto-Rey, Frank Kramer
Preventable or undiagnosed visual impairment and blindness affect billion of people worldwide.
2 code implementations • 24 Jun 2020 • Dominik Müller, Iñaki Soto Rey, Frank Kramer
To address this problem, we propose an innovative automated segmentation pipeline for COVID-19 infected regions, which is able to handle small datasets by utilization as variant databases.
2 code implementations • 21 Oct 2019 • Dominik Müller, Frank Kramer
The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation.