162 citations · 229 across the 18 of their papers we have counts for
18 papers
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…
Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning
Adarsh Bhandary Panambur, Prathmesh Madhu, Andreas Maier
Automatic identification of patients with luminal and non-luminal subtypes during a routine mammography screening can support clinicians in streamlining breast cancer therapy plann…
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…
Employing Graph Representations for Cell-level Characterization of Melanoma MELC Samples
Luis Carlos Rivera Monroy, Leonhard Rist, Martin Eberhardt +5
Histopathology imaging is crucial for the diagnosis and treatment of skin diseases. For this reason, computer-assisted approaches have gained popularity and shown promising results…
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…
PoCaP Corpus: A Multimodal Dataset for Smart Operating Room Speech Assistant using Interventional Radiology Workflow Analysis
Kubilay Can Demir, Matthias May, Axel Schmid +5
This paper presents a new multimodal interventional radiology dataset, called PoCaP (Port Catheter Placement) Corpus. This corpus consists of speech and audio signals in German, X-…
ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation
Mingxuan Gu, Sulaiman Vesal, Mareike Thies +5
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain. Contrastive learning (CL) in the context of UDA…
Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection
Florian Thamm, Oliver Taubmann, Markus Jürgens +5
Ischemic strokes are often caused by large vessel occlusions (LVOs), which can be visualized and diagnosed with Computed Tomography Angiography scans. As time is brain, a fast, acc…
An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes
Florian Thamm, Markus Jürgens, Oliver Taubmann +4
During the diagnosis of ischemic strokes, the Circle of Willis and its surrounding vessels are the arteries of interest. Their visualization in case of an acute stroke is often ena…
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…
Limited Parameter Denoising for Low-dose X-ray Computed Tomography Using Deep Reinforcement Learning
Mayank Patwari, Ralf Gutjahr, Rainer Raupach +1
The use of deep learning has successfully solved several problems in the field of medical imaging. Deep learning has been applied to the CT denoising problem successfully. However,…
Deep learning-guided weighted averaging for signal dropout compensation in diffusion-weighted imaging of the liver
Fasil Gadjimuradov, Thomas Benkert, Marcel Dominik Nickel +3
Purpose: To develop an algorithm for the retrospective correction of signal dropout artifacts in abdominal diffusion-weighted imaging (DWI) resulting from cardiac motion. Methods:…
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…
Superpixel Pre-Segmentation of HER2 Slides for Efficient Annotation
Mathias Öttl, Jana Mönius, Christian Marzahl +8
Supervised deep learning has shown state-of-the-art performance for medical image segmentation across different applications, including histopathology and cancer research; however,…
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…
Segmentation of the Carotid Lumen and Vessel Wall using Deep Learning and Location Priors
Florian Thamm, Felix Denzinger, Leonhard Rist +3
In this report we want to present our method and results for the Carotid Artery Vessel Wall Segmentation Challenge. We propose an image-based pipeline utilizing the U-Net architect…