762 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- German Cancer Research CenterDE20 papers
- Heidelberg UniversityDE9 papers
- Technical University of MunichDE7 papers
- University Hospital HeidelbergDE7 papers
- National Center for Tumor DiseasesDE6 papers
- Imperial College LondonGB5 papers
- Technische Universität DresdenDE5 papers
- Ludwig-Maximilians-Universität MünchenDE4 papers
- University Hospital Carl Gustav CarusDE4 papers
- Essen University HospitalDE3 papers
- Frankfurt Cancer InstituteDE3 papers
- Goethe University FrankfurtDE3 papers
25 papers
Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings
Christian Grashei, Christian Brechenmacher, Rao Muhammad Umer +4
Pathology foundation models (FMs) have driven significant progress in computational pathology. However, these high-performing models can easily exceed a billion parameters and prod…
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…
MultiMAE for Brain MRIs: Robustness to Missing Inputs Using Multi-Modal Masked Autoencoder
Ayhan Can Erdur, Christian Beischl, Daniel Scholz +4
Missing input sequences are common in medical imaging data, posing a challenge for deep learning models reliant on complete input data. In this work, inspired by MultiMAE [2], we d…
Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting
Alexander Koebler, Ralf Gross, Florian Buettner +1
Flexible industrial production systems will play a central role in the future of manufacturing due to higher product individualization and customization. A key component in such sy…
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…
Using UK Biobank data to establish population-specific atlases from whole body MRI
Sophie Starck, Vasiliki Sideri-Lampretsa, Jessica J. M. Ritter +4
Reliable reference data in medical imaging is largely unavailable. Developing tools that allow for the comparison of individual patient data to reference data has a high potential…
Atlas-Based Interpretable Age Prediction In Whole-Body MR Images
Sophie Starck, Yadunandan Vivekanand Kini, Jessica Johanna Maria Ritter +3
Age prediction is an important part of medical assessments and research. It can aid in detecting diseases as well as abnormal ageing by highlighting potential discrepancies between…
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…
FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy
André Ferreira, Jianning Li, Kelsey L. Pomykala +3
With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is ver…
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…
blob loss: instance imbalance aware loss functions for semantic segmentation
Florian Kofler, Suprosanna Shit, Ivan Ezhov +14
Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volum…
The use of deep learning in interventional radiotherapy (brachytherapy): a review with a focus on open source and open data
Tobias Fechter, Ilias Sachpazidis, Dimos Baltas
Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in inte…
Characterization of pixelated silicon detectors for daily quality assurance measurements in proton therapy
Isabelle Schilling, Claus Maximillian Bäcker, Christian Bäumer +4
The advanced imaging and delivery techniques in proton therapy allow conformal high-dose irradiation of the target volume with high accuracy using pencil beam scanning or beam shap…
Polygenic Risk Score in Africa Population: Progress and challenges
Yagoub Adam, Suraju Sadeeq, Judit Kumuthini +8
Polygenic risk score (PRS) analysis is a powerful method been used to estimate an individual's genetic risk towards targeted traits. PRS analysis could be used to obtain evidence o…
Medical Deep Learning -- A systematic Meta-Review
Jan Egger, Christina Gsaxner, Antonio Pepe +5
Deep learning (DL) has remarkably impacted several different scientific disciplines over the last few years. E.g., in image processing and analysis, DL algorithms were able to outp…
Measuring breathing induced oesophageal motion and its dosimetric impact
Tobias Fechter, Sonja Adebahr, Anca-Ligia Grosu +1
Stereotactic body radiation therapy allows for a precise and accurate dose delivery. Organ motion during treatment bears the risk of undetected high dose healthy tissue exposure. A…
Netboost: Boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington's disease
Pascal Schlosser, Jochen Knaus, Maximilian Schmutz +7
Background: State-of-the art selection methods fail to identify weak but cumulative effects of features found in many high-dimensional omics datasets. Nevertheless, these features…
Automated brain extraction of multi-sequence MRI using artificial neural networks
Fabian Isensee, Marianne Schell, Irada Tursunova +10
Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences the accuracy of downstream analyses. The majority of brain extraction a…
MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging -- design, implementation and application on the example of DCE-MRI
Charlotte Debus, Ralf Floca, Michael Ingrisch +4
Many medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in DCE MRI/CT, ADC calcu…
Digital high-pass filter deconvolution by means of an infinite impulse response filter
P. Födisch, J. Wohsmann, B. Lange +3
In the application of semiconductor detectors, the charge-sensitive amplifier is widely used in front-end electronics. The output signal is shaped by a typical exponential decay. D…
Charge-sensitive front-end electronics with operational amplifiers for CdZnTe detectors
P. Födisch, M. Berthel, B. Lange +3
Cadmium zinc telluride (CdZnTe, CZT) radiation detectors are suitable for a variety of applications, due to their high spatial resolution and spectroscopic energy performance at ro…
Range prediction for tissue mixtures based on dual-energy CT
Christian Möhler, Patrick Wohlfahrt, Christian Richter +1
The use of dual-energy CT (DECT) potentially decreases range uncertainties in proton and ion therapy treatment planning via determination of the involved physical target quantities…
A synchronous Gigabit Ethernet protocol stack for high-throughput UDP/IP applications
P. Födisch, B. Lange, J. Sandmann +3
State of the art detector readout electronics require high-throughput data acquisition (DAQ) systems. In many applications, e. g. for medical imaging, the front-end electronics are…