Search Results for author: Katharina Breininger

Found 52 papers, 14 papers with code

Leveraging image captions for selective whole slide image annotation

1 code implementation8 Jul 2024 Jingna Qiu, Marc Aubreville, Frauke Wilm, Mathias Öttl, Jonas Utz, Maja Schlereth, Katharina Breininger

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 to the extensive image size and the significant manual work involved in the annotation.

Diversity Image Captioning +3

Style-Extracting Diffusion Models for Semi-Supervised Histopathology Segmentation

no code implementations21 Mar 2024 Mathias Öttl, Frauke Wilm, Jana Steenpass, Jingna Qiu, Matthias Rübner, Arndt Hartmann, Matthias Beckmann, Peter Fasching, Andreas Maier, Ramona Erber, Bernhard Kainz, Katharina Breininger

Specifically, we utilize 1) a style conditioning mechanism which allows to inject style information of previously unseen images during image generation and 2) a content conditioning which can be targeted to a downstream task, e. g., layout for segmentation.

Image Generation Segmentation

Analysing Diffusion Segmentation for Medical Images

no code implementations21 Mar 2024 Mathias Öttl, Siyuan Mei, Frauke Wilm, Jana Steenpass, Matthias Rübner, Arndt Hartmann, Matthias Beckmann, Peter Fasching, Andreas Maier, Ramona Erber, Katharina Breininger

However, there is a notable lack of analysis and discussions on the differences between diffusion segmentation and image generation, and thorough evaluations are missing that distinguish the improvements these architectures provide for segmentation in general from their benefit for diffusion segmentation specifically.

Denoising Image Generation +3

Re-identification from histopathology images

no code implementations19 Mar 2024 Jonathan Ganz, Jonas Ammeling, Samir Jabari, Katharina Breininger, Marc Aubreville

We predicted the source patient of a slide with F1 scores of 50. 16 % and 52. 30 % on the LSCC and LUAD datasets, respectively, and with 62. 31 % on our meningioma dataset.

Rethinking U-net Skip Connections for Biomedical Image Segmentation

no code implementations13 Feb 2024 Frauke Wilm, Jonas Ammeling, Mathias Öttl, Rutger H. J. Fick, Marc Aubreville, Katharina Breininger

Previous works showed that the trained network layers differ in their susceptibility to this domain shift, e. g., shallow layers are more affected than deeper layers.

Image Segmentation Segmentation +1

Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis

1 code implementation6 Oct 2023 Glejdis Shkëmbi, Johanna P. Müller, Zhe Li, Katharina Breininger, Peter Schüffler, Bernhard Kainz

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 guidance.

Data Augmentation Multiple Instance Learning +1

Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation

1 code implementation14 Jul 2023 Jingna Qiu, Frauke Wilm, Mathias Öttl, Maja Schlereth, Chang Liu, Tobias Heimann, Marc Aubreville, Katharina Breininger

We find that the efficiency of this method highly depends on the choice of AL step size (i. e., the combination of region size and the number of selected regions per WSI), and a suboptimal AL step size can result in redundant annotation requests or inflated computation costs.

Active Learning Informativeness +2

Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset

1 code implementation11 Jan 2023 Frauke Wilm, Marco Fragoso, Christof A. Bertram, Nikolas Stathonikos, Mathias Öttl, Jingna Qiu, Robert Klopfleisch, Andreas Maier, Katharina Breininger, Marc Aubreville

Additionally, to quantify the inherent scanner-induced domain shift, we train a tumor segmentation network on each scanner subset and evaluate the performance both in- and cross-domain.

Domain Generalization Tumor Segmentation

Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays

1 code implementation15 Dec 2022 Jonas Ammeling, Lars-Henning Schmidt, Jonathan Ganz, Tanja Niedermair, Christoph Brochhausen-Delius, Christian Schulz, Katharina Breininger, Marc Aubreville

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 pathology tasks such as outcome prediction and cancer subtyping problems.

Multiple Instance Learning Survival Prediction +1

Deep learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-Free Object Detector

1 code implementation12 Dec 2022 Marc Aubreville, Jonathan Ganz, Jonas Ammeling, Taryn A. Donovan, Rutger H. J. Fick, Katharina Breininger, Christof A. Bertram

In this work, we perform, for the first time, automatic subtyping of mitotic figures into normal and atypical categories according to characteristic morphological appearances of the different phases of mitosis.

object-detection Object Detection

Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks

no code implementations11 Nov 2022 Mathias Öttl, Jana Mönius, Matthias Rübner, Carol I. Geppert, Jingna Qiu, Frauke Wilm, Arndt Hartmann, Matthias W. Beckmann, Peter A. Fasching, Andreas Maier, Ramona Erber, Katharina Breininger

We show the suitability of Generative Adversarial Networks (GANs) and especially diffusion models to create realistic images based on subtype-conditioning for the use case of HER2-stained histopathology.

Segmentation Tumor Segmentation

Employing Graph Representations for Cell-level Characterization of Melanoma MELC Samples

no code implementations10 Nov 2022 Luis Carlos Rivera Monroy, Leonhard Rist, Martin Eberhardt, Christian Ostalecki, Andreas Baur, Julio Vera, Katharina Breininger, Andreas Maier

For this reason, computer-assisted approaches have gained popularity and shown promising results in tasks such as segmentation and classification of skin disorders.

Classification Graph Neural Network

A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT

no code implementations15 Sep 2022 Stefan Ploner, Siyu Chen, Jungeun Won, Lennart Husvogt, Katharina Breininger, Julia Schottenhamml, James Fujimoto, Andreas Maier

Optical coherence tomography (OCT) is a micrometer-scale, volumetric imaging modality that has become a clinical standard in ophthalmology.


CAD-RADS Scoring using Deep Learning and Task-Specific Centerline Labeling

no code implementations8 Feb 2022 Felix Denzinger, Michael Wels, Oliver Taubmann, Mehmet A. Gülsün, Max Schöbinger, Florian André, Sebastian J. Buss, Johannes Görich, Michael Sühling, Andreas Maier, Katharina Breininger

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 diagnosis is high.

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

1 code implementation27 Jan 2022 Frauke Wilm, Marco Fragoso, Christian Marzahl, Jingna Qiu, Chloé Puget, Laura Diehl, Christof A. Bertram, Robert Klopfleisch, Andreas Maier, Katharina Breininger, Marc Aubreville

Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging.

whole slide images

Initial Investigations Towards Non-invasive Monitoring of Chronic Wound Healing Using Deep Learning and Ultrasound Imaging

no code implementations25 Jan 2022 Maja Schlereth, Daniel Stromer, Yash Mantri, Jason Tsujimoto, Katharina Breininger, Andreas Maier, Caesar Anderson, Pranav S. Garimella, Jesse V. Jokerst

We conclude that deep learning-supported analysis of non-invasive ultrasound images is a promising area of research to automatically extract cross-sectional wound size and depth information with potential value in monitoring response to therapy.

Superpixel Pre-Segmentation of HER2 Slides for Efficient Annotation

no code implementations19 Jan 2022 Mathias Öttl, Jana Mönius, Christian Marzahl, Matthias Rübner, Carol I. Geppert, Arndt Hartmann, Matthias W. Beckmann, Peter Fasching, Andreas Maier, Ramona Erber, Katharina Breininger

When evaluating the approaches on fully manually annotated images, we observe that the autoencoder-based superpixels achieve a 23% increase in boundary F1 score compared to the baseline SLIC superpixels.

Clustering Denoising +5

Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge

no code implementations25 Aug 2021 Frauke Wilm, Christian Marzahl, Katharina Breininger, Marc Aubreville

This work presents a mitotic figure detection algorithm developed as a baseline for the challenge, based on domain adversarial training.

Domain Generalization

Quantifying the Scanner-Induced Domain Gap in Mitosis Detection

2 code implementations30 Mar 2021 Marc Aubreville, Christof Bertram, Mitko Veta, Robert Klopfleisch, Nikolas Stathonikos, Katharina Breininger, Natalie ter Hoeve, Francesco Ciompi, Andreas Maier

Hypothesizing that the scanner device plays a decisive role in this effect, we evaluated the susceptibility of a standard mitosis detection approach to the domain shift introduced by using a different whole slide scanner.

Mitosis Detection

Learning to be EXACT, Cell Detection for Asthma on Partially Annotated Whole Slide Images

no code implementations13 Jan 2021 Christian Marzahl, Christof A. Bertram, Frauke Wilm, Jörn Voigt, Ann K. Barton, Robert Klopfleisch, Katharina Breininger, Andreas Maier, Marc Aubreville

We evaluated our pipeline in a cross-validation setup with a fixed training set using a dataset of six equine WSIs of which four are partially annotated and used for training, and two fully annotated WSI are used for validation and testing.

Cell Detection object-detection +2

Deep Learning Algorithms for Coronary Artery Plaque Characterisation from CCTA Scans

no code implementations13 Dec 2019 Felix Denzinger, Michael Wels, Katharina Breininger, Anika Reidelshöfer, Joachim Eckert, Michael Sühling, Axel Schmermund, Andreas Maier

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 subject of current research.

Management Texture Classification

Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging

no code implementations19 Nov 2019 Bernhard Stimpel, Christopher Syben, Tobias Würfl, Katharina Breininger, Philipp Hoelter, Arnd Dörfler, Andreas Maier

Additionally, a weighting scheme in the loss computation that favors high-frequency structures is proposed to focus on the important details and contours in projection imaging.

Image Enhancement Translation

What Do We Really Need? Degenerating U-Net on Retinal Vessel Segmentation

no code implementations6 Nov 2019 Weilin Fu, Katharina Breininger, Zhaoya Pan, Andreas Maier

Results show that for retinal vessel segmentation on DRIVE database, U-Net does not degenerate until surprisingly acute conditions: one level, one filter in convolutional layers, and one training sample.

Retinal Vessel Segmentation Segmentation

A Divide-and-Conquer Approach towards Understanding Deep Networks

no code implementations14 Jul 2019 Weilin Fu, Katharina Breininger, Roman Schaffert, Nishant Ravikumar, Andreas Maier

We start with a high-performance U-Net and show by step-by-step conversion that we are able to divide the network into modules of known operators.

Image Segmentation Retinal Vessel Segmentation +1

Decoupling Respiratory and Angular Variation in Rotational X-ray Scans Using a Prior Bilinear Model

no code implementations30 Apr 2018 Tobias Geimer, Paul Keall, Katharina Breininger, Vincent Caillet, Michelle Dunbar, Christoph Bert, Andreas Maier

Data-driven respiratory signal extraction from rotational X-ray scans is a challenge as angular effects overlap with respiration-induced change in the scene.

Frangi-Net: A Neural Network Approach to Vessel Segmentation

no code implementations9 Nov 2017 Weilin Fu, Katharina Breininger, Tobias Würfl, Nishant Ravikumar, Roman Schaffert, Andreas Maier

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 the original Frangi filter.

Precision Learning: Reconstruction Filter Kernel Discretization

no code implementations17 Oct 2017 Christopher Syben, Bernhard Stimpel, Katharina Breininger, Tobias Würfl, Rebecca Fahrig, Arnd Dörfler, Andreas Maier

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.

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