68 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Michael Götz2 profiles5 · h 4
- Meinrad Beer2 profiles3 · h 18
- Alexander Muckenhuber2 · h 2
- Catharina Silvia Lisson2
- Christoph Gerhard Lisson2
- Constantin Ulrich2 · h 12
- J. Kleesiek2 · h 11
- Klaus H. Maier-Hein2 · h 32
- L. Gallée2 · h 3
- Maximilian Fischer2 · h 5
- Peter J. Schüffler2 profiles2 · h 25
- R. Braren2 · h 37
- Universität UlmDE9 papers
- German Cancer Research CenterDE4 papers
- Deutsches Konsortium für Translationale KrebsforschungDE3 papers
- Heidelberg UniversityDE3 papers
- Technical University of MunichDE3 papers
- Forschungszentrum JülichDE2 papers
- National Center for Tumor DiseasesDE2 papers
- RWTH Aachen UniversityDE2 papers
- Technische Hochschule UlmDE2 papers
- University Hospital HeidelbergDE2 papers
- University Medical Center FreiburgDE2 papers
- Agency for Science, Technology and ResearchSG1 paper
14 papers
Responsible Trauma Research: Designing Effective and Sustainable Virtual Reality Exposure Studies
Annalisa Degenhard, Sophia Ppali, Fotis Liarokapis +3
Virtual reality exposure therapy (VRET) enables controlled exposure to trauma-related stimuli to facilitate memory access and emotional processing. However, the field remains under…
Mathematical and Computational Nuclear Oncology: Toward Optimized Radiopharmaceutical Therapy via Digital Twins
Marc Ryhiner, Yangmeihui Song, Babak Saboury +3
This article presents the general framework of theranostic digital twins (TDTs) in computational nuclear medicine, designed to support clinical decision-making and improve cancer p…
Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning
Maximilian Fischer, Peter Neher, Peter Schüffler +9
Accurate diagnosis of disease often depends on the exhaustive examination of Whole Slide Images (WSI) at microscopic resolution. Efficient handling of these data-intensive images r…
Unlocking the Potential of Digital Pathology: Novel Baselines for Compression
Maximilian Fischer, Peter Neher, Peter Schüffler +13
Digital pathology offers a groundbreaking opportunity to transform clinical practice in histopathological image analysis, yet faces a significant hurdle: the substantial file sizes…
ezyMRI: How to build an MRI machine from scratch -- Experience from a four-day hackathon
Shaoying Huang, José Miguel Algarín, Joseba Alonso +25
Nuclear magnetic resonance instruments are becoming available to the do-it-yourself community. The challenges encountered in the endeavor to build a magnetic resonance imaging inst…
Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
Dominic LaBella, Ujjwal Baid, Omaditya Khanna +119
We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in…
Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model
Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson +5
Due to the sensitive nature of medicine, it is particularly important and highly demanded that AI methods are explainable. This need has been recognised and there is great research…
An in-silico approach to meniscus tissue regeneration: Modeling, numerical simulation, and experimental analysis
Elise Grosjean, Alex Keilmann, Henry Jäger +10
We develop a model the dynamics of human mesenchymal stem cells (hMSCs) and chondrocytes evolving in a nonwoven polyethylene terephtalate (PET) scaffold impregnated with hyaluron a…
On Leakage in Machine Learning Pipelines
Leonard Sasse, Eliana Nicolaisen-Sobesky, Juergen Dukart +9
Machine learning (ML) provides powerful tools for predictive modeling. ML's popularity stems from the promise of sample-level prediction with applications across a variety of field…
Interpretable Medical Image Classification using Prototype Learning and Privileged Information
Luisa Gallee, Meinrad Beer, Michael Goetz
Interpretability is often an essential requirement in medical imaging. Advanced deep learning methods are required to address this need for explainability and high performance. In…
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging
Daniel Wolf, Tristan Payer, Catharina Silvia Lisson +4
Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning mod…
A Novel Bis-Coumarin Targets Multiple Tyrosine Kinases of Key Signaling Pathways in Melanoma and Inhibits Melanoma Cell Survival, Proliferation, and Migration
Qurat-ul-Ain, Abhijit Basu, Sebistein Iben +2
Melanoma is one of the most dangerous skin malignancies due to its high metastatic tendency and high mortality. Activation of key signaling pathways enforcing melanoma progression…
Netboost: Boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington's disease
Pascal Schlosser, Jochen Knaus, Maximilian Schmutz +7
Background: State-of-the art selection methods fail to identify weak but cumulative effects of features found in many high-dimensional omics datasets. Nevertheless, these features…
Algorithm guided outlining of 105 pancreatic cancer liver metastases in Ultrasound
Alexander Hann, Lucas Bettac, Mark M. Haenle +6
Manual segmentation of hepatic metastases in ultrasound images acquired from patients suffering from pancreatic cancer is common practice. Semiautomatic measurements promising assi…