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
22 papers · 1 filter
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…
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…
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…
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…
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…
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-…
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…
Application-driven Validation of Posteriors in Inverse Problems
Tim J. Adler, Jan-Hinrich Nölke, Annika Reinke +8
Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist. In response, poste…
TUNeS: A Temporal U-Net with Self-Attention for Video-based Surgical Phase Recognition
Isabel Funke, Dominik Rivoir, Stefanie Krell +1
Objective: To enable context-aware computer assistance in the operating room of the future, cognitive systems need to understand automatically which surgical phase is being perform…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
Learning shape distributions from large databases of healthy organs: applications to zero-shot and few-shot abnormal pancreas detection
Rebeca Vétil, Clément Abi Nader, Alexandre Bône +4
We propose a scalable and data-driven approach to learn shape distributions from large databases of healthy organs. To do so, volumetric segmentation masks are embedded into a comm…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
Chinedu Innocent Nwoye, Deepak Alapatt, Tong Yu +59
Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works…
SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstruction
P. J. "Eddie'' Edwards, Dimitris Psychogyios, Stefanie Speidel +2
In computer vision, reference datasets have been highly successful in promoting algorithmic development in stereo reconstruction. Surgical scenes gives rise to specific problems, i…
Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration
Dominik Rivoir, Sebastian Bodenstedt, Felix von Bechtolsheim +3
Estimating the remaining surgery duration (RSD) during surgical procedures can be useful for OR planning and anesthesia dose estimation. With the recent success of deep learning-ba…
Video-based surgical skill assessment using 3D convolutional neural networks
Isabel Funke, Sören Torge Mees, Jürgen Weitz +1
Purpose: A profound education of novice surgeons is crucial to ensure that surgical interventions are effective and safe. One important aspect is the teaching of technical skills f…
Assessing robustness of radiomic features by image perturbation
Alex Zwanenburg, Stefan Leger, Linda Agolli +4
Image features need to be robust against differences in positioning, acquisition and segmentation to ensure reproducibility. Radiomic models that only include robust features can b…
Temporal coherence-based self-supervised learning for laparoscopic workflow analysis
Isabel Funke, Alexander Jenke, Sören Torge Mees +3
In order to provide the right type of assistance at the right time, computer-assisted surgery systems need context awareness. To achieve this, methods for surgical workflow analysi…
Image biomarker standardisation initiative
Alex Zwanenburg, Stefan Leger, Martin Vallières +1
The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of image biomarkers from acquir…