66 citations · 122 across the 20 of their papers we have counts for
20 papers
Zero-shot System for Automatic Body Region Detection for Volumetric CT and MR Images
Farnaz Khun Jush, Grit Werner, Mark Klemens +1
Reliable identification of anatomical body regions is a prerequisite for many automated medical imaging workflows, yet existing solutions remain heavily dependent on unreliable DIC…
Revisiting Integration of Image and Metadata for DICOM Series Classification: Cross-Attention and Dictionary Learning
Tuan Truong, Melanie Dohmen, Sara Lorio +1
Automated identification of DICOM image series is essential for large-scale medical image analysis, quality control, protocol harmonization, and reliable downstream processing. How…
Towards Selection of Large Multimodal Models as Engines for Burned-in Protected Health Information Detection in Medical Images
Tuan Truong, Guillermo Jimenez Perez, Pedro Osorio +1
The detection of Protected Health Information (PHI) in medical imaging is critical for safeguarding patient privacy and ensuring compliance with regulatory frameworks. Traditional…
Content-based 3D Image Retrieval and a ColBERT-inspired Re-ranking for Tumor Flagging and Staging
Farnaz Khun Jush, Steffen Vogler, Matthias Lenga
The increasing volume of medical images poses challenges for radiologists in retrieving relevant cases. Content-based image retrieval (CBIR) systems offer potential for efficient a…
Exploring AI-based System Design for Pixel-level Protected Health Information Detection in Medical Images
Tuan Truong, Ivo M. Baltruschat, Mark Klemens +2
De-identification of medical images is a critical step to ensure privacy during data sharing in research and clinical settings. The initial step in this process involves detecting…
Five Pitfalls When Assessing Synthetic Medical Images with Reference Metrics
Melanie Dohmen, Tuan Truong, Ivo M. Baltruschat +1
Reference metrics have been developed to objectively and quantitatively compare two images. Especially for evaluating the quality of reconstructed or compressed images, these metri…
Content-Based Image Retrieval for Multi-Class Volumetric Radiology Images: A Benchmark Study
Farnaz Khun Jush, Steffen Vogler, Tuan Truong +1
While content-based image retrieval (CBIR) has been extensively studied in natural image retrieval, its application to medical images presents ongoing challenges, primarily due to…
Similarity and Quality Metrics for MR Image-To-Image Translation
Melanie Dohmen, Mark A. Klemens, Ivo M. Baltruschat +2
Image-to-image translation can create large impact in medical imaging, as images can be synthetically transformed to other modalities, sequence types, higher resolutions or lower n…
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives
Ivo M. Baltruschat, Parvaneh Janbakhshi, Matthias Lenga
This work addresses the Brain Magnetic Resonance Image Synthesis for Tumor Segmentation (BraSyn) challenge, which was hosted as part of the Brain Tumor Segmentation (BraTS) challen…
Benchmarking Pretrained Vision Embeddings for Near- and Duplicate Detection in Medical Images
Tuan Truong, Farnaz Khun Jush, Matthias Lenga
Near- and duplicate image detection is a critical concern in the field of medical imaging. Medical datasets often contain similar or duplicate images from various sources, which ca…
Medical Image Retrieval Using Pretrained Embeddings
Farnaz Khun Jush, Tuan Truong, Steffen Vogler +1
A wide range of imaging techniques and data formats available for medical images make accurate retrieval from image databases challenging. Efficient retrieval systems are crucial i…
Uncertainty Estimation in Contrast-Enhanced MR Image Translation with Multi-Axis Fusion
Ivo M. Baltruschat, Parvaneh Janbakhshi, Melanie Dohmen +1
In recent years, deep learning has been applied to a wide range of medical imaging and image processing tasks. In this work, we focus on the estimation of epistemic uncertainty for…
fRegGAN with K-space Loss Regularization for Medical Image Translation
Ivo M. Baltruschat, Felix Kreis, Alexander Hoelscher +2
Generative adversarial networks (GANs) have shown remarkable success in generating realistic images and are increasingly used in medical imaging for image-to-image translation task…
Transfer Learning for Segmentation Problems: Choose the Right Encoder and Skip the Decoder
Jonas Dippel, Matthias Lenga, Thomas Goerttler +2
It is common practice to reuse models initially trained on different data to increase downstream task performance. Especially in the computer vision domain, ImageNet-pretrained wei…
Fused Audio Instance and Representation for Respiratory Disease Detection
Tuan Truong, Matthias Lenga, Antoine Serrurier +1
Audio-based classification techniques on body sounds have long been studied to aid in the diagnosis of respiratory diseases. While most research is centered on the use of cough as…
How Transferable Are Self-supervised Features in Medical Image Classification Tasks?
Tuan Truong, Sadegh Mohammadi, Matthias Lenga
Transfer learning has become a standard practice to mitigate the lack of labeled data in medical classification tasks. Whereas finetuning a downstream task using supervised ImageNe…
Multi-Resolution 3D Convolutional Neural Networks for Automatic Coronary Centerline Extraction in Cardiac CT Angiography Scans
Zohaib Salahuddin, Matthias Lenga, Hannes Nickisch
We propose a deep learning-based automatic coronary artery tree centerline tracker (AuCoTrack) extending the vessel tracker by Wolterink (arXiv:1810.03143). A dual pathway Convolut…
Are American options European after all?
Sören Christensen, Jan Kallsen, Matthias Lenga
We call a given American option representable if there exists a European claim which dominates the American payoff at any time and such that the values of the two options coincide…
Continual Learning for Domain Adaptation in Chest X-ray Classification
Matthias Lenga, Heinrich Schulz, Axel Saalbach
Over the last years, Deep Learning has been successfully applied to a broad range of medical applications. Especially in the context of chest X-ray classification, results have bee…
Deep Learning Based Rib Centerline Extraction and Labeling
Matthias Lenga, Tobias Klinder, Christian Bürger +3
Automated extraction and labeling of rib centerlines is a typically needed prerequisite for more advanced assisted reading tools that help the radiologist to efficiently inspect al…