4.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Heidelberg UniversityDE42 papers
- University Hospital HeidelbergDE37 papers
- German Cancer Research CenterDE29 papers
- University Hospital Carl Gustav CarusDE19 papers
- Nationales Centrum für Tumorerkrankungen DresdenDE17 papers
- Else Kröner Fresenius Center for Digital HealthDE14 papers
- Technische Universität DresdenDE11 papers
- Technical University of MunichDE9 papers
- RWTH Aachen UniversityDE8 papers
- University College LondonGB8 papers
- University Medical Centre MannheimDE7 papers
- Deutsches Konsortium für Translationale KrebsforschungDE6 papers
54 papers
Transformation Behavior of Images in Latent Space
Christian Zöllner, Mozzam Motiwala, Aysel Ahadova +4
Training of neural networks for histopathology classification tasks typically relies on data encoding into latent space, which reduces complexity and improves performance. There ar…
Quantifying the human visual exposome with vision language models
Christian Rominger, Andreas R. Schwerdtfeger, Malay Gaherwar Singh +5
The visual environment is a fundamental yet unquantified determinant of mental health. While the concept of the environmental exposome is well established, current methods rely on…
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…
Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence
Maximilian Ferle, Jonas Ader, Thomas Wiemers +7
Risk stratification is a key tool in clinical decision-making, yet current approaches often fail to translate sophisticated survival analysis into actionable clinical criteria. We…
Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge
Tobias Rueckert, David Rauber, Raphaela Maerkl +58
Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minim…
Helium Range Viability for Online Range Probing in Mixed Carbon-Helium Beams
Jennifer J. Hardt, Alexander A. Pryanichnikov, Oliver Jäkel +2
Background: Recently, mixed carbon-helium beams were proposed for range verification in carbon ion therapy: Helium, with three times the range of carbon, serves as an on-line range…
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…
SynthRAD2025 Grand Challenge dataset: generating synthetic CTs for radiotherapy
Adrian Thummerer, Erik van der Bijl, Arthur Jr Galapon +16
Medical imaging is essential in modern radiotherapy, supporting diagnosis, treatment planning, and monitoring. Synthetic imaging, particularly synthetic computed tomography (sCT),…
Novel computational workflows for natural and biomedical image processing based on hypercomplex algebras
Nektarios A. Valous, Eckhard Hitzer, Dragoş Duşe +12
Hypercomplex image processing extends conventional techniques in a unified paradigm encompassing algebraic and geometric principles. This work leverages quaternions and the two-dim…
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…
Cluster-based human-in-the-loop strategy for improving machine learning-based circulating tumor cell detection in liquid biopsy
Hümeyra Husseini-Wüsthoff, Sabine Riethdorf, Andreas Schneeweiss +5
Detection and differentiation of circulating tumor cells (CTCs) and non-CTCs in blood draws of cancer patients pose multiple challenges. While the gold standard relies on tedious m…
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…
Deep intra-operative illumination calibration of hyperspectral cameras
Alexander Baumann, Leonardo Ayala, Alexander Studier-Fischer +6
Hyperspectral imaging (HSI) is emerging as a promising novel imaging modality with various potential surgical applications. Currently available cameras, however, suffer from poor i…
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…
AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients
Silvia Seidlitz, Katharina Hölzl, Ayca von Garrel +10
With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and those at high risk of death is a challenge of high socioeconomic importance. Gi…
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…
Predicting Progression Events in Multiple Myeloma from Routine Blood Work
Maximilian Ferle, Nora Grieb, Markus Kreuz +5
The ability to accurately predict disease progression is paramount for optimizing multiple myeloma patient care. This study introduces a hybrid neural network architecture, combini…
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…
Image segmentation of treated and untreated tumor spheroids by Fully Convolutional Networks
Matthias Streller, Soňa Michlíková, Willy Ciecior +4
Multicellular tumor spheroids (MCTS) are advanced cell culture systems for assessing the impact of combinatorial radio(chemo)therapy. They exhibit therapeutically relevant in-vivo-…
Clinical Melanoma Diagnosis with Artificial Intelligence: Insights from a Prospective Multicenter Study
Lukas Heinlein, Roman C. Maron, Achim Hekler +27
Early detection of melanoma, a potentially lethal type of skin cancer with high prevalence worldwide, improves patient prognosis. In retrospective studies, artificial intelligence…