162 citations · 514 across the 55 of their papers we have counts for
27 papers · 1 filter
A Self-supervised Multimodal Deep Learning Approach to Differentiate Post-radiotherapy Progression from Pseudoprogression in Glioblastoma
Ahmed Gomaa, Yixing Huang, Pluvio Stephan +19
Accurate differentiation of pseudoprogression (PsP) from True Progression (TP) following radiotherapy (RT) in glioblastoma (GBM) patients is crucial for optimal treatment planning.…
Domain and Content Adaptive Convolutions for Cross-Domain Adenocarcinoma Segmentation
Frauke Wilm, Mathias Öttl, Marc Aubreville +1
Recent advances in computer-aided diagnosis for histopathology have been largely driven by the use of deep learning models for automated image analysis. While these networks can pe…
Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma
Ahmed Gomaa, Yixing Huang, Amr Hagag +16
Background: This research aims to improve glioblastoma survival prediction by integrating MR images, clinical and molecular-pathologic data in a transformer-based deep learning mod…
Analysing Diffusion Segmentation for Medical Images
Mathias Öttl, Siyuan Mei, Frauke Wilm +8
Denoising Diffusion Probabilistic models have become increasingly popular due to their ability to offer probabilistic modeling and generate diverse outputs. This versatility inspir…
Rethinking U-net Skip Connections for Biomedical Image Segmentation
Frauke Wilm, Jonas Ammeling, Mathias Öttl +3
The U-net architecture has significantly impacted deep learning-based segmentation of medical images. Through the integration of long-range skip connections, it facilitated the pre…
Automated Volume Corrected Mitotic Index Calculation Through Annotation-Free Deep Learning using Immunohistochemistry as Reference Standard
Jonas Ammeling, Moritz Hecker, Jonathan Ganz +4
The volume-corrected mitotic index (M/V-Index) was shown to provide prognostic value in invasive breast carcinomas. However, despite its prognostic significance, it is not establis…
Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis
Glejdis Shkëmbi, Johanna P. Müller, Zhe Li +3
Breast cancer is a major concern for women's health globally, with axillary lymph node (ALN) metastasis identification being critical for prognosis evaluation and treatment guidanc…
Focus on Content not Noise: Improving Image Generation for Nuclei Segmentation by Suppressing Steganography in CycleGAN
Jonas Utz, Tobias Weise, Maja Schlereth +5
Annotating nuclei in microscopy images for the training of neural networks is a laborious task that requires expert knowledge and suffers from inter- and intra-rater variability, e…
Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset
Frauke Wilm, Marco Fragoso, Christof A. Bertram +7
In histopathology, scanner-induced domain shifts are known to impede the performance of trained neural networks when tested on unseen data. Multi-domain pre-training or dedicated d…
Deep Learning-Based Automatic Assessment of AgNOR-scores in Histopathology Images
Jonathan Ganz, Karoline Lipnik, Jonas Ammeling +10
Nucleolar organizer regions (NORs) are parts of the DNA that are involved in RNA transcription. Due to the silver affinity of associated proteins, argyrophilic NORs (AgNORs) can be…
Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology
Frauke Wilm, Marco Fragoso, Christof A. Bertram +7
Computer-aided systems in histopathology are often challenged by various sources of domain shift that impact the performance of these algorithms considerably. We investigated the p…
Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks
Mathias Öttl, Jana Mönius, Matthias Rübner +9
Tumor segmentation in histopathology images is often complicated by its composition of different histological subtypes and class imbalance. Oversampling subtypes with low prevalenc…
A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT
Stefan Ploner, Siyu Chen, Jungeun Won +5
Optical coherence tomography (OCT) is a micrometer-scale, volumetric imaging modality that has become a clinical standard in ophthalmology. OCT instruments image by raster-scanning…
Mitosis domain generalization in histopathology images -- The MIDOG challenge
Marc Aubreville, Nikolas Stathonikos, Christof A. Bertram +32
The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitoti…
CAD-RADS Scoring using Deep Learning and Task-Specific Centerline Labeling
Felix Denzinger, Michael Wels, Oliver Taubmann +8
With coronary artery disease (CAD) persisting to be one of the leading causes of death worldwide, interest in supporting physicians with algorithms to speed up and improve diagnosi…
Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging
Maja Schlereth, Daniel Stromer, Katharina Breininger +4
Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society. They can lead to severe progressive muscle weakness, muscle degeneration, contract…
Initial Investigations Towards Non-invasive Monitoring of Chronic Wound Healing Using Deep Learning and Ultrasound Imaging
Maja Schlereth, Daniel Stromer, Yash Mantri +6
Chronic wounds including diabetic and arterial/venous insufficiency injuries have become a major burden for healthcare systems worldwide. Demographic changes suggest that wound car…
Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset
Frauke Wilm, Marco Fragoso, Christian Marzahl +8
Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approac…
First steps on Gamification of Lung Fluid Cells Annotations in the Flower Domain
Sonja Kunzmann, Christian Marzahl, Felix Denzinger +5
Annotating data, especially in the medical domain, requires expert knowledge and a lot of effort. This limits the amount and/or usefulness of available medical data sets for experi…
Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge
Frauke Wilm, Christian Marzahl, Katharina Breininger +1
Assessing the Mitotic Count has a known high degree of intra- and inter-rater variability. Computer-aided systems have proven to decrease this variability and reduce labeling time.…
Automatic and explainable grading of meningiomas from histopathology images
Jonathan Ganz, Tobias Kirsch, Lucas Hoffmann +7
Meningioma is one of the most prevalent brain tumors in adults. To determine its malignancy, it is graded by a pathologist into three grades according to WHO standards. This grade…
Learning to be EXACT, Cell Detection for Asthma on Partially Annotated Whole Slide Images
Christian Marzahl, Christof A. Bertram, Frauke Wilm +6
Asthma is a chronic inflammatory disorder of the lower respiratory tract and naturally occurs in humans and animals including horses. The annotation of an asthma microscopy whole s…
Automatic CAD-RADS Scoring Using Deep Learning
Felix Denzinger, Michael Wels, Katharina Breininger +7
Coronary CT angiography (CCTA) has established its role as a non-invasive modality for the diagnosis of coronary artery disease (CAD). The CAD-Reporting and Data System (CAD-RADS)…
Deep Learning Algorithms for Coronary Artery Plaque Characterisation from CCTA Scans
Felix Denzinger, Michael Wels, Katharina Breininger +5
Analysing coronary artery plaque segments with respect to their functional significance and therefore their influence to patient management in a non-invasive setup is an important…