9.8k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Heidelberg UniversityDE146 papers
- German Cancer Research CenterDE68 papers
- National Center for Tumor DiseasesDE37 papers
- University Medical Center UtrechtNL18 papers
- Technical University of MunichDE15 papers
- Else Kröner Fresenius Center for Digital HealthDE14 papers
- Harvard UniversityUS14 papers
- University Hospital Carl Gustav CarusDE14 papers
- University Medical Centre MannheimDE14 papers
- RWTH Aachen UniversityDE13 papers
- Utrecht UniversityNL13 papers
- Karlsruhe Institute of TechnologyDE12 papers
31 papers · 1 filter
The Missing Piece: A Case for Pre-Training in 3D Medical Object Detection
Katharina Eckstein, Constantin Ulrich, Michael Baumgartner +5
Large-scale pre-training holds the promise to advance 3D medical object detection, a crucial component of accurate computer-aided diagnosis. Yet, it remains underexplored compared…
A Tutorial on MRI Reconstruction: From Modern Methods to Clinical Implications
Tolga Çukur, Salman U. H. Dar, Valiyeh Ansarian Nezhad +4
MRI is an indispensable clinical tool, offering a rich variety of tissue contrasts to support broad diagnostic and research applications. Clinical exams routinely acquire multiple…
Implicit neural representations for accurate estimation of the standard model of white matter
Tom Hendriks, Gerrit Arends, Edwin Versteeg +3
Diffusion magnetic resonance imaging (dMRI) enables non-invasive investigation of tissue microstructure. The Standard Model (SM) of white matter aims to disentangle dMRI signal con…
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…
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…
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…
Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts
Lisa Hemforth, Baptiste Couvy-Duchesne, Kevin De Matos +34
Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population.…
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Hartmut Häntze, Lina Xu, Christian J. Mertens +29
Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans. Materials and Methods: The model was trained on 1,200 manually annotated 3D axial…
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…
Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study
Christoph Wies, Lucas Schneider, Sarah Haggenmueller +6
Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in dia…
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…
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…
Unsupervised Domain Transfer with Conditional Invertible Neural Networks
Kris K. Dreher, Leonardo Ayala, Melanie Schellenberg +11
Synthetic medical image generation has evolved as a key technique for neural network training and validation. A core challenge, however, remains in the domain gap between simulatio…
MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation
Constantin Ulrich, Fabian Isensee, Tassilo Wald +3
The medical imaging community generates a wealth of datasets, many of which are openly accessible and annotated for specific diseases and tasks such as multi-organ or lesion segmen…
Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging
Yusheng Zhou, Hao Li, Jianan Liu +6
Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, superv…
Adaptive Diffusion Priors for Accelerated MRI Reconstruction
Alper Güngör, Salman UH Dar, Şaban Öztürk +4
Deep MRI reconstruction is commonly performed with conditional models that de-alias undersampled acquisitions to recover images consistent with fully-sampled data. Since conditiona…
Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection
Ishaan Bhat, Josien P. W. Pluim, Max A. Viergever +1
Deep learning techniques show success in detecting objects in medical images, but still suffer from false-positive predictions that may hinder accurate diagnosis. The estimated unc…
Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation
Jianan Liu, Hao Li, Tao Huang +6
High-resolution (HR) magnetic resonance imaging is critical in aiding doctors in their diagnoses and image-guided treatments. However, acquiring HR images can be time-consuming and…
Performance of a GPU- and Time-Efficient Pseudo 3D Network for Magnetic Resonance Image Super-Resolution and Motion Artifact Reduction
Hao Li, Jianan Liu, Marianne Schell +8
Shortening acquisition time and reducing motion artifacts are the most critical challenges in magnetic resonance imaging (MRI). Deep learning-based image restoration has emerged as…
Robust deep learning-based semantic organ segmentation in hyperspectral images
Silvia Seidlitz, Jan Sellner, Jan Odenthal +11
Semantic image segmentation is an important prerequisite for context-awareness and autonomous robotics in surgery. The state of the art has focused on conventional RGB video data a…
Machine learning-based analysis of hyperspectral images for automated sepsis diagnosis
Maximilian Dietrich, Silvia Seidlitz, Nicholas Schreck +16
Sepsis is a leading cause of mortality and critical illness worldwide. While robust biomarkers for early diagnosis are still missing, recent work indicates that hyperspectral imagi…
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas +55
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely inv…
nnDetection: A Self-configuring Method for Medical Object Detection
Michael Baumgartner, Paul F. Jaeger, Fabian Isensee +1
Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions…