38 citations · 47 across the 12 of their papers we have counts for
13 papers
Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks
Santhosh Parampottupadam, Andres Martinez, Dimitrios Bounias +3
Federated learning (FL) enables multi-institutional training on clinical text without sharing raw data, but gradient inversion can reconstruct sensitive information from shared mod…
TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation
Tristan Kirscher, Alexandra Ertl, Klaus Maier-Hein +3
Pancreatic ductal adenocarcinoma (PDAC) segmentation on contrast-enhanced CT is inherently ambiguous: inter-rater disagreement among experts reflects genuine uncertainty rather tha…
Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos
Jieyun Bai, Zihao Zhou, Yitong Tang +60
A substantial proportion (45\%) of maternal deaths, neonatal deaths, and stillbirths occur during the intrapartum phase, with a particularly high burden in low- and middle-income c…
Kaapana: A Comprehensive Open-Source Platform for Integrating AI in Medical Imaging Research Environments
Ünal Akünal, Markus Bujotzek, Stefan Denner +8
Developing generalizable AI for medical imaging requires both access to large, multi-center datasets and standardized, reproducible tooling within research environments. However, l…
Medical Image De-Identification Benchmark Challenge
Linmin Pei, Granger Sutton, Michael Rutherford +67
The de-identification (deID) of protected health information (PHI) and personally identifiable information (PII) is a fundamental requirement for sharing medical images, particular…
PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
Jieyun Bai, Zihao Zhou, Zhanhong Ou +26
Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology…
Visual Prompt Engineering for Vision Language Models in Radiology
Stefan Denner, Markus Bujotzek, Dimitrios Bounias +3
Medical image classification plays a crucial role in clinical decision-making, yet most models are constrained to a fixed set of predefined classes, limiting their adaptability to…
Automated Ensemble Multimodal Machine Learning for Healthcare
Fergus Imrie, Stefan Denner, Lucas S. Brunschwig +2
The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approac…
Fair Evaluation of Federated Learning Algorithms for Automated Breast Density Classification: The Results of the 2022 ACR-NCI-NVIDIA Federated Learning Challenge
Kendall Schmidt, Benjamin Bearce, Ken Chang +21
The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due t…
Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain
Markus R. Bujotzek, Ünal Akünal, Stefan Denner +17
Objective: Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments…
Automatic classification of prostate MR series type using image content and metadata
Deepa Krishnaswamy, Bálint Kovács, Stefan Denner +6
With the wealth of medical image data, efficient curation is essential. Assigning the sequence type to magnetic resonance images is necessary for scientific studies and artificial…
Leveraging Foundation Models for Content-Based Image Retrieval in Radiology
Stefan Denner, David Zimmerer, Dimitrios Bounias +8
Content-based image retrieval (CBIR) has the potential to significantly improve diagnostic aid and medical research in radiology. However, current CBIR systems face limitations due…
Efficient Large Scale Medical Image Dataset Preparation for Machine Learning Applications
Stefan Denner, Jonas Scherer, Klaus Kades +7
In the rapidly evolving field of medical imaging, machine learning algorithms have become indispensable for enhancing diagnostic accuracy. However, the effectiveness of these algor…