215 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Heidelberg UniversityDE14 papers
- National Center for Tumor DiseasesDE14 papers
- University Hospital HeidelbergDE14 papers
- University Hospital Carl Gustav CarusDE11 papers
- Fresenius (Germany)DE9 papers
- RWTH Aachen UniversityDE9 papers
- Universitätsklinikum AachenDE5 papers
- German Cancer Research CenterDE4 papers
- Deutsches Herzzentrum MünchenDE3 papers
- Stanford UniversityUS3 papers
- Technical University of MunichDE3 papers
- Technische Universität DresdenDE3 papers
19 papers
Gut decisions based on the liver: A radiomics approach to boost colorectal cancer screening
Anna Hinterberger, Jonas Bohn, Dasha Trofimova +13
Non-invasive colorectal cancer (CRC) screening represents a key opportunity to improve colonoscopy participation rates and reduce CRC mortality. This study explores the potential o…
Hallucination Filtering in Radiology Vision-Language Models Using Discrete Semantic Entropy
Patrick Wienholt, Sophie Caselitz, Robert Siepmann +6
To determine whether using discrete semantic entropy (DSE) to reject questions likely to generate hallucinations can improve the accuracy of black-box vision-language models (VLMs)…
Resolution scaling governs DINOv3 transfer performance in chest radiograph classification
Soroosh Tayebi Arasteh, Mina Shaigan, Christiane Kuhl +3
Self-supervised learning (SSL) has improved visual representation learning, but its value in chest radiography remains uncertain. DINOv3 extends earlier SSL models through Gram-anc…
Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge
Max Kirchner, Hanna Hoffmann, Alexander C. Jenke +16
Developing generalizable surgical AI requires multi-institutional data, yet privacy constraints preclude direct data sharing, making Federated Learning (FL) a natural candidate. It…
MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification
Patrick Wienholt, Christiane Kuhl, Jakob Nikolas Kather +2
Deep neural networks excel in radiological image classification but frequently suffer from poor interpretability, limiting clinical acceptance. We present MedicalPatchNet, an inher…
Generating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraints
Waldemar Hahn, Jan-Niklas Eckardt, Christoph Röllig +3
The generation of synthetic clinical trial data offers a promising approach to mitigating privacy concerns and data accessibility limitations in medical research. However, ensuring…
Enhanced Anomaly Detection for Capsule Endoscopy Using Ensemble Learning Strategies
Julia Werner, Christoph Gerum, Jorg Nick +4
Capsule endoscopy is a method to capture images of the gastrointestinal tract and screen for diseases which might remain hidden if investigated with standard endoscopes. Due to the…
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2
Gustav Müller-Franzes, Firas Khader, Robert Siepmann +4
MRI and CT are essential clinical cross-sectional imaging techniques for diagnosing complex conditions. However, large 3D datasets with annotations for deep learning are scarce. Wh…
Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors
Jacqueline Lammert, Nicole Pfarr, Leonid Kuligin +16
Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poo…
RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering
Soroosh Tayebi Arasteh, Mahshad Lotfinia, Keno Bressem +7
Large language models (LLMs) often generate outdated or inaccurate information based on static training datasets. Retrieval-augmented generation (RAG) mitigates this by integrating…
Superhuman performance in urology board questions by an explainable large language model enabled for context integration of the European Association of Urology guidelines: the UroBot study
Martin J. Hetz, Nicolas Carl, Sarah Haggenmüller +4
Large Language Models (LLMs) are revolutionizing medical Question-Answering (medQA) through extensive use of medical literature. However, their performance is often hampered by out…
Benchmarking Pathology Feature Extractors for Whole Slide Image Classification
Georg Wölflein, Dyke Ferber, Asier R. Meneghetti +6
Weakly supervised whole slide image classification is a key task in computational pathology, which involves predicting a slide-level label from a set of image patches constituting…
Large Language Models Streamline Automated Machine Learning for Clinical Studies
Soroosh Tayebi Arasteh, Tianyu Han, Mahshad Lotfinia +4
A knowledge gap persists between machine learning (ML) developers (e.g., data scientists) and practitioners (e.g., clinicians), hampering the full utilization of ML for clinical da…
Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images
Soroosh Tayebi Arasteh, Leo Misera, Jakob Nikolas Kather +2
Pre-training datasets, like ImageNet, have become the gold standard in medical image analysis. However, the emergence of self-supervised learning (SSL), which leverages unlabeled d…
The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
Jun Ma, Ronald Xie, Shamini Ayyadhury +37
Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or req…
Using Multiple Dermoscopic Photographs of One Lesion Improves Melanoma Classification via Deep Learning: A Prognostic Diagnostic Accuracy Study
Achim Hekler, Roman C. Maron, Sarah Haggenmüller +28
Background: Convolutional neural network (CNN)-based melanoma classifiers face several challenges that limit their usefulness in clinical practice. Objective: To investigate the im…
Fibroglandular Tissue Segmentation in Breast MRI using Vision Transformers -- A multi-institutional evaluation
Gustav Müller-Franzes, Fritz Müller-Franzes, Luisa Huck +9
Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background parenchymal enhancement. I…
Diffusion Probabilistic Models beat GANs on Medical Images
Gustav Müller-Franzes, Jan Moritz Niehues, Firas Khader +8
The success of Deep Learning applications critically depends on the quality and scale of the underlying training data. Generative adversarial networks (GANs) can generate arbitrary…
Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels
Soroosh Tayebi Arasteh, Peter Isfort, Marwin Saehn +6
Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training…