Search Results for author: Marc Aubreville

Found 45 papers, 21 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

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

Nuclear Pleomorphism in Canine Cutaneous Mast Cell Tumors: Comparison of Reproducibility and Prognostic Relevance between Estimates, Manual Morphometry and Algorithmic Morphometry

no code implementations26 Sep 2023 Andreas Haghofer, Eda Parlak, Alexander Bartel, Taryn A. Donovan, Charles-Antoine Assenmacher, Pompei Bolfa, Michael J. Dark, Andrea Fuchs-Baumgartinger, Andrea Klang, Kathrin Jäger, Robert Klopfleisch, Sophie Merz, Barbara Richter, F. Yvonne Schulman, Hannah Janout, Jonathan Ganz, Josef Scharinger, Marc Aubreville, Stephan M. Winkler, Matti Kiupel, Christof A. Bertram

We assessed the following nuclear evaluation methods for measurement accuracy, reproducibility, and prognostic utility: 1) anisokaryosis (karyomegaly) estimates by 11 pathologists; 2) gold standard manual morphometry of at least 100 nuclei; 3) practicable manual morphometry with stratified sampling of 12 nuclei by 9 pathologists; and 4) automated morphometry using a deep learning-based segmentation algorithm.


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

Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models

1 code implementation1 Jun 2023 Pablo Pernias, Dominic Rampas, Mats L. Richter, Christopher J. Pal, Marc Aubreville

This highly compressed representation of an image provides much more detailed guidance compared to latent representations of language and this significantly reduces the computational requirements to achieve state-of-the-art results.

Image Compression Image Generation

Why is the winner the best?

no code implementations CVPR 2023 Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David G. Ellis, Sandy Engelhardt, Melanie Ganz, Noha Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, Ali Emre Kavur, Oldřich Kodym, Michal Kozubek, Jianning Li, Hongwei Li, Jun Ma, Carlos Martín-Isla, Bjoern Menze, Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patiño, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin Van Wijnen, Martin Wagner, Donglai Wei, Amine Yamlahi, Moi Hoon Yap, Chun Yuan, Maximilian Zenk, Aneeq Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, Jihoon Cho, Chanyeol Choi, Qi Dou, Ivan Ezhov, Christoph M. Friedrich, Clifton Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García Faura, Maria Grammatikopoulou, SeulGi Hong, Mostafa Jahanifar, Ikbeom Jang, Abdolrahim Kadkhodamohammadi, Inha Kang, Florian Kofler, Satoshi Kondo, Hugo Kuijf, Mingxing Li, Minh Huan Luu, Tomaž Martinčič, Pedro Morais, Mohamed A. Naser, Bruno Oliveira, David Owen, Subeen Pang, Jinah Park, Sung-Hong Park, Szymon Płotka, Elodie Puybareau, Nasir Rajpoot, Kanghyun Ryu, Numan Saeed, Adam Shephard, Pengcheng Shi, Dejan Štepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Helene Urien, João L. Vilaça, Kareem Abdul Wahid, Haojie Wang, Jiacheng Wang, Liansheng Wang, Xiyue Wang, Benedikt Wiestler, Marek Wodzinski, Fangfang Xia, Juanying Xie, Zhiwei Xiong, Sen yang, Yanwu Yang, Zixuan Zhao, Klaus Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein

The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.

Benchmarking Multi-Task Learning

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

A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces

4 code implementations14 Nov 2022 Dominic Rampas, Pablo Pernias, Marc Aubreville

Recent advancements in the domain of text-to-image synthesis have culminated in a multitude of enhancements pertaining to quality, fidelity, and diversity.

Conditional Image Generation Denoising +2

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

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

CLCNet: Deep learning-based Noise Reduction for Hearing Aids using Complex Linear Coding

no code implementations28 Jan 2020 Hendrik Schröter, Tobias Rosenkranz, Alberto N. Escalante B., Marc Aubreville, Andreas Maier

To improve monaural speech enhancement in noisy environments, we propose CLCNet, a framework based on complex valued linear coding.

Speech Enhancement

Learning New Tricks from Old Dogs -- Inter-Species, Inter-Tissue Domain Adaptation for Mitotic Figure Assessment

no code implementations25 Nov 2019 Marc Aubreville, Christof A. Bertram, Samir Jabari, Christian Marzahl, Robert Klopfleisch, Andreas Maier

We were able to show that domain adversarial training considerably improves accuracy when applying mitotic figure classification learned from the canine on the human data sets (up to +12. 8% in accuracy) and is thus a helpful method to transfer knowledge from existing data sets to new tissue types and species.

Domain Adaptation

SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images

1 code implementation7 Feb 2018 Marc Aubreville, Christof Bertram, Robert Klopfleisch, Andreas Maier

It provides single-click annotations as well as a blind mode for multi-annotations, where the expert is directly shown the microscopy image containing the cells that he has not yet rated.

Mitosis Detection whole slide images

Motion Artifact Detection in Confocal Laser Endomicroscopy Images

no code implementations3 Nov 2017 Maike P. Stoeve, Marc Aubreville, Nicolai Oetter, Christian Knipfer, Helmut Neumann, Florian Stelzle, Andreas Maier

Confocal Laser Endomicroscopy (CLE), an optical imaging technique allowing non-invasive examination of the mucosa on a (sub)cellular level, has proven to be a valuable diagnostic tool in gastroenterology and shows promising results in various anatomical regions including the oral cavity.

Artifact Detection

A Guided Spatial Transformer Network for Histology Cell Differentiation

no code implementations26 Jul 2017 Marc Aubreville, Maximilian Krappmann, Christof Bertram, Robert Klopfleisch, Andreas Maier

The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells and granulocytes) and their position within an image.

General Classification

Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images -- A Cross-Site Robustness Assessment

no code implementations25 Jul 2017 Marc Aubreville, Miguel Goncalves, Christian Knipfer, Nicolai Oetter, Tobias Wuerfl, Helmut Neumann, Florian Stelzle, Christopher Bohr, Andreas Maier

We find that the network trained on the oral cavity data reaches an accuracy of 89. 45% and an area-under-the-curve (AUC) value of 0. 955, when applied on the vocal cords data.

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