2024
Puget, Chloé; Ganz, Jonathan; Ostermaier, Julian; Conrad, Thomas; Parlak, Eda; Bertram, Christof A.; Kiupel, Matti; Breininger, Katharina; Aubreville, Marc; Klopfleisch, Robert
In: Veterinary Pathology, pp. 03009858241286806, 2024, ISSN: 0300-9858, 1544-2217.
Abstract | Links | BibTeX | Tags:
@article{puget_artificial_2024,
title = {Artificial intelligence can be trained to predict textitc-KIT -11 mutational status of canine mast cell tumors from hematoxylin and eosin-stained histological slides},
author = {Chloé Puget and Jonathan Ganz and Julian Ostermaier and Thomas Conrad and Eda Parlak and Christof A. Bertram and Matti Kiupel and Katharina Breininger and Marc Aubreville and Robert Klopfleisch},
url = {https://journals.sagepub.com/doi/10.1177/03009858241286806},
doi = {10.1177/03009858241286806},
issn = {0300-9858, 1544-2217},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-21},
journal = {Veterinary Pathology},
pages = {03009858241286806},
abstract = {Numerous prognostic factors are currently assessed histologically and immunohistochemically in canine mast cell tumors (MCTs) to evaluate clinical behavior. In addition, polymerase chain reaction (PCR) is often performed to detect internal tandem duplication (ITD) mutations in exon 11 of the c-KIT gene ( c-KIT-11-ITD) to predict the therapeutic response to tyrosine kinase inhibitors. This project aimed at training deep learning models (DLMs) to identify MCTs with c-KIT-11-ITD solely based on morphology. Hematoxylin and eosin (HE) stained slides of 368 cutaneous, subcutaneous, and mucocutaneous MCTs (195 with ITD and 173 without) were stained consecutively in 2 different laboratories and scanned with 3 different slide scanners. This resulted in 6 data sets (stain-scanner variations representing diagnostic institutions) of whole-slide images. DLMs were trained with single and mixed data sets and their performances were assessed under stain-scanner variations (domain shifts). The DLM correctly classified HE slides according to their c-KIT-11-ITD status in up to 87% of cases with a 0.90 sensitivity and a 0.83 specificity. A relevant performance drop could be observed when the stain-scanner combination of training and test data set differed. Multi-institutional data sets improved the average accuracy but did not reach the maximum accuracy of algorithms trained and tested on the same stain-scanner variant (ie, intra-institutional). In summary, DLM-based morphological examination can predict c-KIT-11-ITD with high accuracy in canine MCTs in HE slides. However, staining protocol and scanner type influence accuracy. Larger data sets of scans from different laboratories and scanners may lead to more robust DLMs to identify c- KIT mutations in HE slides.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Aubreville, Marc; Ganz, Jonathan; Ammeling, Jonas; Rosbach, Emely; Gehrke, Thomas; Scherzad, Agmal; Hackenberg, Stephan; Goncalves, Miguel
Prediction of tumor board procedural recommendations using large language models Journal Article
In: European Archives of Oto-Rhino-Laryngology, 2024, ISSN: 1434-4726.
Abstract | Links | BibTeX | Tags:
@article{aubreville_prediction_2024,
title = {Prediction of tumor board procedural recommendations using large language models},
author = {Marc Aubreville and Jonathan Ganz and Jonas Ammeling and Emely Rosbach and Thomas Gehrke and Agmal Scherzad and Stephan Hackenberg and Miguel Goncalves},
url = {https://doi.org/10.1007/s00405-024-08947-9},
doi = {10.1007/s00405-024-08947-9},
issn = {1434-4726},
year = {2024},
date = {2024-09-01},
journal = {European Archives of Oto-Rhino-Laryngology},
abstract = {Multidisciplinary tumor boards are meetings where a team of medical specialists, including medical oncologists, radiation oncologists, radiologists, surgeons, and pathologists, collaborate to determine the best treatment plan for cancer patients. While decision-making in this context is logistically and cost-intensive, it has a significant positive effect on overall cancer survival.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ganz, Jonathan; Ammeling, Jonas; Jabari, Samir; Breininger, Katharina; Aubreville, Marc
Re-identification from histopathology images Journal Article
In: Medical Image Analysis, pp. 103335, 2024, ISSN: 13618415.
@article{ganz_re-identification_2024,
title = {Re-identification from histopathology images},
author = {Jonathan Ganz and Jonas Ammeling and Samir Jabari and Katharina Breininger and Marc Aubreville},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1361841524002603},
doi = {10.1016/j.media.2024.103335},
issn = {13618415},
year = {2024},
date = {2024-09-01},
urldate = {2024-09-20},
journal = {Medical Image Analysis},
pages = {103335},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Aubreville, Marc; Ganz, Jonathan; Ammeling, Jonas; Kaltenecker, Christopher; Bertram, Christof A.
Model-based Cleaning of the QUILT-1M Pathology Dataset for Text-Conditional Image Synthesis Proceedings Article
In: Medical Imaging with Deep Learning, Paris, France, 2024.
Abstract | Links | BibTeX | Tags:
@inproceedings{aubreville_model-based_2024,
title = {Model-based Cleaning of the QUILT-1M Pathology Dataset for Text-Conditional Image Synthesis},
author = {Marc Aubreville and Jonathan Ganz and Jonas Ammeling and Christopher Kaltenecker and Christof A. Bertram},
url = {https://openreview.net/forum?id=m7wYKrUjzV},
year = {2024},
date = {2024-07-01},
booktitle = {Medical Imaging with Deep Learning},
address = {Paris, France},
abstract = {The QUILT-1M dataset is the first openly available dataset containing images harvested from various online sources. While it provides a huge data variety, the image quality and composition is highly heterogeneous, impacting its utility for text-conditional image synthesis. We propose an automatic pipeline that provides predictions of the most common impurities within the images, e.g., visibility of narrators, desktop environment and pathology software, or text within the image. Additionally, we propose to use semantic alignment filtering of the image-text pairs. Our findings demonstrate that by rigorously filtering the dataset, there is a substantial enhancement of image fidelity in text-to-image tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Glahn, Imaine; Haghofer, Andreas; Donovan, Taryn A.; Degasperi, Brigitte; Bartel, Alexander; Kreilmeier-Berger, Theresa; Hyndman, Philip S.; Janout, Hannah; Assenmacher, Charles-Antoine; Bartenschlager, Florian; Bolfa, Pompei; Dark, Michael J.; Klang, Andrea; Klopfleisch, Robert; Merz, Sophie; Richter, Barbara; Schulman, F. Yvonne; Ganz, Jonathan; Scharinger, Josef; Aubreville, Marc; Winkler, Stephan M.; Bertram, Christof A.
Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility Journal Article
In: Veterinary Sciences, vol. 11, no. 6, pp. 278, 2024, ISSN: 2306-7381.
Abstract | Links | BibTeX | Tags:
@article{glahn_automated_2024,
title = {Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility},
author = {Imaine Glahn and Andreas Haghofer and Taryn A. Donovan and Brigitte Degasperi and Alexander Bartel and Theresa Kreilmeier-Berger and Philip S. Hyndman and Hannah Janout and Charles-Antoine Assenmacher and Florian Bartenschlager and Pompei Bolfa and Michael J. Dark and Andrea Klang and Robert Klopfleisch and Sophie Merz and Barbara Richter and F. Yvonne Schulman and Jonathan Ganz and Josef Scharinger and Marc Aubreville and Stephan M. Winkler and Christof A. Bertram},
url = {https://www.mdpi.com/2306-7381/11/6/278},
doi = {10.3390/vetsci11060278},
issn = {2306-7381},
year = {2024},
date = {2024-06-01},
urldate = {2024-06-19},
journal = {Veterinary Sciences},
volume = {11},
number = {6},
pages = {278},
abstract = {The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists’ NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists’ estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required.},
keywords = {},
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Aubreville, Marc; Stathonikos, Nikolas; Donovan, Taryn A.; Klopfleisch, Robert; Ammeling, Jonas; Ganz, Jonathan; Wilm, Frauke; Veta, Mitko; Jabari, Samir; Eckstein, Markus; Annuscheit, Jonas; Krumnow, Christian; Bozaba, Engin; Çayır, Sercan; Gu, Hongyan; Chen, Xiang ‘Anthony’; Jahanifar, Mostafa; Shephard, Adam; Kondo, Satoshi; Kasai, Satoshi; Kotte, Sujatha; Saipradeep, V. G.; Lafarge, Maxime W.; Koelzer, Viktor H.; Wang, Ziyue; Zhang, Yongbing; Yang, Sen; Wang, Xiyue; Breininger, Katharina; Bertram, Christof A.
In: Medical Image Analysis, vol. 94, pp. 103155, 2024, ISSN: 13618415.
@article{aubreville_domain_2024,
title = {Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge},
author = {Marc Aubreville and Nikolas Stathonikos and Taryn A. Donovan and Robert Klopfleisch and Jonas Ammeling and Jonathan Ganz and Frauke Wilm and Mitko Veta and Samir Jabari and Markus Eckstein and Jonas Annuscheit and Christian Krumnow and Engin Bozaba and Sercan Çayır and Hongyan Gu and Xiang ‘Anthony’ Chen and Mostafa Jahanifar and Adam Shephard and Satoshi Kondo and Satoshi Kasai and Sujatha Kotte and V. G. Saipradeep and Maxime W. Lafarge and Viktor H. Koelzer and Ziyue Wang and Yongbing Zhang and Sen Yang and Xiyue Wang and Katharina Breininger and Christof A. Bertram},
url = {https://linkinghub.elsevier.com/retrieve/pii/S136184152400080X},
doi = {10.1016/j.media.2024.103155},
issn = {13618415},
year = {2024},
date = {2024-05-01},
urldate = {2024-03-27},
journal = {Medical Image Analysis},
volume = {94},
pages = {103155},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Aubreville, Marc; Pan, Zhaoya; Sievert, Matti; Ammeling, Jonas; Ganz, Jonathan; Oetter, Nicolai; Stelzle, Florian; Frenken, Ann-Kathrin; Breininger, Katharina; Goncalves, Miguel
Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors Book Section
In: Maier, Andreas; Deserno, Thomas M.; Handels, Heinz; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2024, pp. 143–148, Springer Fachmedien Wiesbaden, Wiesbaden, 2024, ISBN: 9783658440367 9783658440374.
@incollection{maier_few_2024,
title = {Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors},
author = {Marc Aubreville and Zhaoya Pan and Matti Sievert and Jonas Ammeling and Jonathan Ganz and Nicolai Oetter and Florian Stelzle and Ann-Kathrin Frenken and Katharina Breininger and Miguel Goncalves},
editor = {Andreas Maier and Thomas M. Deserno and Heinz Handels and Klaus Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {https://link.springer.com/10.1007/978-3-658-44037-4_42},
doi = {10.1007/978-3-658-44037-4_42},
isbn = {9783658440367 9783658440374},
year = {2024},
date = {2024-01-01},
urldate = {2024-02-21},
booktitle = {Bildverarbeitung für die Medizin 2024},
pages = {143–148},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
keywords = {},
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}
Ammeling, Jonas; Hecker, Moritz; Ganz, Jonathan; Donovan, Taryn A.; Klopfleisch, Robert; Bertram, Christof A.; Breininger, Katharina; Aubreville, Marc
Automated Mitotic Index Calculation via Deep Learning and Immunohistochemistry Book Section
In: Maier, Andreas; Deserno, Thomas M.; Handels, Heinz; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2024, pp. 123–128, Springer Fachmedien Wiesbaden, Wiesbaden, 2024, ISBN: 9783658440367 9783658440374.
@incollection{maier_automated_2024,
title = {Automated Mitotic Index Calculation via Deep Learning and Immunohistochemistry},
author = {Jonas Ammeling and Moritz Hecker and Jonathan Ganz and Taryn A. Donovan and Robert Klopfleisch and Christof A. Bertram and Katharina Breininger and Marc Aubreville},
editor = {Andreas Maier and Thomas M. Deserno and Heinz Handels and Klaus Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {https://link.springer.com/10.1007/978-3-658-44037-4_37},
doi = {10.1007/978-3-658-44037-4_37},
isbn = {9783658440367 9783658440374},
year = {2024},
date = {2024-01-01},
urldate = {2024-02-21},
booktitle = {Bildverarbeitung für die Medizin 2024},
pages = {123–128},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
keywords = {},
pubstate = {published},
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Ganz, Jonathan; Puget, Chloé; Ammeling, Jonas; Parlak, Eda; Kiupel, Matti; Bertram, Christof A.; Breininger, Katharina; Klopfleisch, Robert; Aubreville, Marc
Assessment of Scanner Domain Shifts in Deep Multiple Instance Learning Book Section
In: Maier, Andreas; Deserno, Thomas M.; Handels, Heinz; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2024, pp. 137–142, Springer Fachmedien Wiesbaden, Wiesbaden, 2024, ISBN: 9783658440367 9783658440374.
@incollection{maier_assessment_2024,
title = {Assessment of Scanner Domain Shifts in Deep Multiple Instance Learning},
author = {Jonathan Ganz and Chloé Puget and Jonas Ammeling and Eda Parlak and Matti Kiupel and Christof A. Bertram and Katharina Breininger and Robert Klopfleisch and Marc Aubreville},
editor = {Andreas Maier and Thomas M. Deserno and Heinz Handels and Klaus Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {https://link.springer.com/10.1007/978-3-658-44037-4_41},
doi = {10.1007/978-3-658-44037-4_41},
isbn = {9783658440367 9783658440374},
year = {2024},
date = {2024-01-01},
urldate = {2024-02-21},
booktitle = {Bildverarbeitung für die Medizin 2024},
pages = {137–142},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
keywords = {},
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2023
Ammeling, Jonas; Manger, Carina; Kwaka, Elias; Krügel, Sebastian; Uhl, Matthias; Kießig, Angelika; Fritz, Alexis; Ganz, Jonathan; Riener, Andreas; Bertram, Christof A.; Breininger, Katharina; Aubreville, Marc
Appealing but Potentially Biasing – Investigation of the Visual Representation of Segmentation Predictions by AI Recommender Systems for Medical Decision Making Proceedings Article
In: MuC ’23: Mensch und Computer 2023, pp. 330–335, ACM, Rapperswil Switzerland, 2023, ISBN: 9798400707711.
@inproceedings{ammeling_appealing_2023,
title = {Appealing but Potentially Biasing - Investigation of the Visual Representation of Segmentation Predictions by AI Recommender Systems for Medical Decision Making},
author = {Jonas Ammeling and Carina Manger and Elias Kwaka and Sebastian Krügel and Matthias Uhl and Angelika Kießig and Alexis Fritz and Jonathan Ganz and Andreas Riener and Christof A. Bertram and Katharina Breininger and Marc Aubreville},
url = {https://dl.acm.org/doi/10.1145/3603555.3608561},
doi = {10.1145/3603555.3608561},
isbn = {9798400707711},
year = {2023},
date = {2023-09-01},
urldate = {2023-09-04},
booktitle = {MuC '23: Mensch und Computer 2023},
pages = {330–335},
publisher = {ACM},
address = {Rapperswil Switzerland},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Aubreville, Marc; Wilm, Frauke; Stathonikos, Nikolas; Breininger, Katharina; Donovan, Taryn A.; Jabari, Samir; Veta, Mitko; Ganz, Jonathan; Ammeling, Jonas; Diest, Paul J. Van; Klopfleisch, Robert; Bertram, Christof A.
A comprehensive multi-domain dataset for mitotic figure detection Journal Article
In: Scientific Data, vol. 10, no. 1, pp. 484, 2023, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags:
@article{aubreville_comprehensive_2023,
title = {A comprehensive multi-domain dataset for mitotic figure detection},
author = {Marc Aubreville and Frauke Wilm and Nikolas Stathonikos and Katharina Breininger and Taryn A. Donovan and Samir Jabari and Mitko Veta and Jonathan Ganz and Jonas Ammeling and Paul J. Van Diest and Robert Klopfleisch and Christof A. Bertram},
url = {https://www.nature.com/articles/s41597-023-02327-4},
doi = {10.1038/s41597-023-02327-4},
issn = {2052-4463},
year = {2023},
date = {2023-07-01},
urldate = {2023-07-26},
journal = {Scientific Data},
volume = {10},
number = {1},
pages = {484},
abstract = {Abstract
The prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species.},
keywords = {},
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}
The prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species.
Ammeling, Jonas; Schmidt, Lars-Henning; Ganz, Jonathan; Niedermair, Tanja; Brochhausen-Delius, Christoph; Schulz, Christian; Breininger, Katharina; Aubreville, Marc
Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays Book Section
In: Deserno, Thomas M.; Handels, Heinz; Maier, Andreas; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2023, pp. 220–225, Springer Fachmedien Wiesbaden, Wiesbaden, 2023, ISBN: 978-3-658-41656-0 978-3-658-41657-7, (Series Title: Informatik aktuell).
@incollection{deserno_attention-based_2023,
title = {Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays},
author = {Jonas Ammeling and Lars-Henning Schmidt and Jonathan Ganz and Tanja Niedermair and Christoph Brochhausen-Delius and Christian Schulz and Katharina Breininger and Marc Aubreville},
editor = {Thomas M. Deserno and Heinz Handels and Andreas Maier and Klaus Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {https://link.springer.com/10.1007/978-3-658-41657-7_48},
doi = {10.1007/978-3-658-41657-7_48},
isbn = {978-3-658-41656-0 978-3-658-41657-7},
year = {2023},
date = {2023-02-01},
urldate = {2023-06-30},
booktitle = {Bildverarbeitung für die Medizin 2023},
pages = {220–225},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
note = {Series Title: Informatik aktuell},
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Aubreville, Marc; Ganz, Jonathan; Ammeling, Jonas; Donovan, Taryn A.; Fick, Rutger Hj.; Breininger, Katharina; Bertram, Christof A.
Deep Learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-free Object Detector Book Section
In: Deserno, Thomas M.; Handels, Heinz; Maier, Andreas; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2023, pp. 189–195, Springer Fachmedien Wiesbaden, Wiesbaden, 2023, ISBN: 978-3-658-41656-0 978-3-658-41657-7, (Series Title: Informatik aktuell).
@incollection{deserno_deep_2023,
title = {Deep Learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-free Object Detector},
author = {Marc Aubreville and Jonathan Ganz and Jonas Ammeling and Taryn A. Donovan and Rutger Hj. Fick and Katharina Breininger and Christof A. Bertram},
editor = {Thomas M. Deserno and Heinz Handels and Andreas Maier and Klaus Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {https://link.springer.com/10.1007/978-3-658-41657-7_40},
doi = {10.1007/978-3-658-41657-7_40},
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year = {2023},
date = {2023-02-01},
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pages = {189–195},
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Ganz, Jonathan; Lipnik, Karoline; Ammeling, Jonas; Richter, Barbara; Puget, Chloé; Parlak, Eda; Diehl, Laura; Klopfleisch, Robert; Donovan, Taryn A.; Kiupel, Matti; Bertram, Christof A.; Breininger, Katharina; Aubreville, Marc
Deep Learning-based Automatic Assessment of AgNOR-scores in Histopathology Images Book Section
In: Deserno, Thomas M.; Handels, Heinz; Maier, Andreas; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2023, pp. 226–231, Springer Fachmedien Wiesbaden, Wiesbaden, 2023, ISBN: 978-3-658-41656-0 978-3-658-41657-7, (Series Title: Informatik aktuell).
@incollection{deserno_deep_2023-1,
title = {Deep Learning-based Automatic Assessment of AgNOR-scores in Histopathology Images},
author = {Jonathan Ganz and Karoline Lipnik and Jonas Ammeling and Barbara Richter and Chloé Puget and Eda Parlak and Laura Diehl and Robert Klopfleisch and Taryn A. Donovan and Matti Kiupel and Christof A. Bertram and Katharina Breininger and Marc Aubreville},
editor = {Thomas M. Deserno and Heinz Handels and Andreas Maier and Klaus Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {https://link.springer.com/10.1007/978-3-658-41657-7_49},
doi = {10.1007/978-3-658-41657-7_49},
isbn = {978-3-658-41656-0 978-3-658-41657-7},
year = {2023},
date = {2023-02-01},
urldate = {2023-06-30},
booktitle = {Bildverarbeitung für die Medizin 2023},
pages = {226–231},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
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}
Ammeling, Jonas; Wilm, Frauke; Ganz, Jonathan; Breininger, Katharina; Aubreville, Marc
Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge Book Section
In: Sheng, Bin; Aubreville, Marc (Ed.): Mitosis Domain Generalization and Diabetic Retinopathy Analysis, vol. 13597, pp. 201–205, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-33657-7 978-3-031-33658-4, (Series Title: Lecture Notes in Computer Science).
@incollection{sheng_reference_2023,
title = {Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge},
author = {Jonas Ammeling and Frauke Wilm and Jonathan Ganz and Katharina Breininger and Marc Aubreville},
editor = {Bin Sheng and Marc Aubreville},
url = {https://link.springer.com/10.1007/978-3-031-33658-4_19},
doi = {10.1007/978-3-031-33658-4_19},
isbn = {978-3-031-33657-7 978-3-031-33658-4},
year = {2023},
date = {2023-01-01},
urldate = {2023-07-02},
booktitle = {Mitosis Domain Generalization and Diabetic Retinopathy Analysis},
volume = {13597},
pages = {201–205},
publisher = {Springer Nature Switzerland},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
2022
Ganz, Jonathan; Bertram, Christof A.; Klopfleisch, Robert; Jabari, Samir; Breininger, Katharina; Aubreville, Marc
Classification of visibility in multi-stain microscopy images Proceedings Article
In: Medical Imaging with Deep Learning 2022, Zurich, 2022.
BibTeX | Tags:
@inproceedings{ganz_classification_2022,
title = {Classification of visibility in multi-stain microscopy images},
author = {Jonathan Ganz and Christof A. Bertram and Robert Klopfleisch and Samir Jabari and Katharina Breininger and Marc Aubreville},
year = {2022},
date = {2022-01-01},
booktitle = {Medical Imaging with Deep Learning 2022},
address = {Zurich},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2021
Ganz, Jonathan; Kirsch, Tobias; Hoffmann, Lucas; Bertram, Christof A; Hoffmann, Christoph; Maier, Andreas; Breininger, Katharina; Blümcke, Ingmar; Jabari, Samir; Aubreville, Marc
Automatic and explainable grading of meningiomas from histopathology images Proceedings Article
In: pp. 69–80, PMLR, 2021, ISBN: 2640-3498.
@inproceedings{ganz_automatic_2021,
title = {Automatic and explainable grading of meningiomas from histopathology images},
author = {Jonathan Ganz and Tobias Kirsch and Lucas Hoffmann and Christof A Bertram and Christoph Hoffmann and Andreas Maier and Katharina Breininger and Ingmar Blümcke and Samir Jabari and Marc Aubreville},
url = {https://proceedings.mlr.press/v156/ganz21a/ganz21a.pdf},
isbn = {2640-3498},
year = {2021},
date = {2021-01-01},
pages = {69–80},
publisher = {PMLR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}