162 citations · 514 across the 55 of their papers we have counts for
24 papers · 1 filter
Evaluating Transformation Models for pCLE Mosaic Registration
Ahmed Aboelela, Johannes Barcsay, Jana Friedhof +3
Confocal Laser Endomicroscopy (CLE) provides real-time, cellular-resolution optical biopsy but has a narrow field of view, which image mosaicing can extend to provide anatomical co…
Cross-Modal MRI Ovary Segmentation in Endometriosis Using Unpaired TVUS Prototype Priors
Xingjian Kang, Lina Felsner, Dominik Perrin +5
Transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) provide complementary information for endometriosis image analysis, yet existing studies mainly focus on single-…
Large-Scale Pre-training Enables Multimodal AI Differentiation of Radiation Necrosis from Brain Metastasis Progression on Routine MRI
Ahmed Gomaa, Annette Schwarz, Ludwig Singer +23
Background: Differentiating radiation necrosis (RN) from tumor progression after stereotactic radiosurgery (SRS) remains a critical challenge in brain metastases. While histopathol…
Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification
Jonathan Ganz, Jonas Ammeling, Emely Rosbach +4
Foundation models (FMs), i.e., models trained on a vast amount of typically unlabeled data, have become popular and available recently for the domain of histopathology. The key ide…
Leveraging image captions for selective whole slide image annotation
Jingna Qiu, Marc Aubreville, Frauke Wilm +4
Acquiring annotations for whole slide images (WSIs)-based deep learning tasks, such as creating tissue segmentation masks or detecting mitotic figures, is a laborious process due t…
On the Value of PHH3 for Mitotic Figure Detection on H&E-stained Images
Jonathan Ganz, Christian Marzahl, Jonas Ammeling +20
The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker as it is a measure for tumor cell proliferation. However…
Style-Extracting Diffusion Models for Semi-Supervised Histopathology Segmentation
Mathias Öttl, Frauke Wilm, Jana Steenpass +9
Deep learning-based image generation has seen significant advancements with diffusion models, notably improving the quality of generated images. Despite these developments, generat…
Re-identification from histopathology images
Jonathan Ganz, Jonas Ammeling, Samir Jabari +2
In numerous studies, deep learning algorithms have proven their potential for the analysis of histopathology images, for example, for revealing the subtypes of tumors or the primar…
Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data Using Graph Neural Networks
Luis Carlos Rivera Monroy, Leonhard Rist, Martin Eberhardt +5
This study leverages graph neural networks to integrate MELC data with Radiomic-extracted features for melanoma classification, focusing on cell-wise analysis. It assesses the effe…
Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors
Marc Aubreville, Zhaoya Pan, Matti Sievert +7
The surgical removal of head and neck tumors requires safe margins, which are usually confirmed intraoperatively by means of frozen sections. This method is, in itself, an oversamp…
Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge
Marc Aubreville, Nikolas Stathonikos, Taryn A. Donovan +27
Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with…
Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation
Jingna Qiu, Frauke Wilm, Mathias Öttl +5
The process of annotating histological gigapixel-sized whole slide images (WSIs) at the pixel level for the purpose of training a supervised segmentation model is time-consuming. R…
Deep learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-Free Object Detector
Marc Aubreville, Jonathan Ganz, Jonas Ammeling +4
Mitotic activity is key for the assessment of malignancy in many tumors. Moreover, it has been demonstrated that the proportion of abnormal mitosis to normal mitosis is of prognost…
Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays
Jonas Ammeling, Lars-Henning Schmidt, Jonathan Ganz +5
Attention-based multiple instance learning (AMIL) algorithms have proven to be successful in utilizing gigapixel whole-slide images (WSIs) for a variety of different computational…
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…
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,…
Quantifying the Scanner-Induced Domain Gap in Mitosis Detection
Marc Aubreville, Christof Bertram, Mitko Veta +6
Automated detection of mitotic figures in histopathology images has seen vast improvements, thanks to modern deep learning-based pipelines. Application of these methods, however, i…
How Many Annotators Do We Need? -- A Study on the Influence of Inter-Observer Variability on the Reliability of Automatic Mitotic Figure Assessment
Frauke Wilm, Christof A. Bertram, Christian Marzahl +23
Density of mitotic figures in histologic sections is a prognostically relevant characteristic for many tumours. Due to high inter-pathologist variability, deep learning-based algor…
Dataset on Bi- and Multi-Nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors
Christof A. Bertram, Taryn A. Donovan, Marco Tecilla +8
Tumor cells with two nuclei (binucleated cells, BiNC) or more nuclei (multinucleated cells, MuNC) indicate an increased amount of cellular genetic material which is thought to faci…
Decoupling Respiratory and Angular Variation in Rotational X-ray Scans Using a Prior Bilinear Model
Tobias Geimer, Paul Keall, Katharina Breininger +4
Data-driven respiratory signal extraction from rotational X-ray scans is a challenge as angular effects overlap with respiration-induced change in the scene. In this paper, we use…
Projection image-to-image translation in hybrid X-ray/MR imaging
Bernhard Stimpel, Christopher Syben, Tobias Würfl +5
The potential benefit of hybrid X-ray and MR imaging in the interventional environment is large due to the combination of fast imaging with high contrast variety. However, a vast a…
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests
Aline Sindel, Katharina Breininger, Johannes Käßer +3
Magnetic resonance imaging (MRI) enables 3-D imaging of anatomical structures. However, the acquisition of MR volumes with high spatial resolution leads to long scan times. To this…
Frangi-Net: A Neural Network Approach to Vessel Segmentation
Weilin Fu, Katharina Breininger, Tobias Würfl +3
In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network ("Frangi-Net"), and illustrate that the Frangi-Net is equivalent to…
Precision Learning: Reconstruction Filter Kernel Discretization
Christopher Syben, Bernhard Stimpel, Katharina Breininger +4
In this paper, we present substantial evidence that a deep neural network will intrinsically learn the appropriate way to discretize the ideal continuous reconstruction filter. Cur…