@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}

}

@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}

}

@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 = {},

pubstate = {published},

tppubtype = {article}

}

@article{krugel_perceived_2024,

title = {Perceived responsibility in AI-supported medicine},

author = {Sebastian Krügel and Jonas Ammeling and Marc Aubreville and Alexis Fritz and Angelika Kießig and Matthias Uhl},

url = {https://link.springer.com/10.1007/s00146-024-01972-6},

doi = {10.1007/s00146-024-01972-6},

issn = {0951-5666, 1435-5655},

year  = {2024},

date = {2024-05-01},

urldate = {2024-05-23},

journal = {AI & SOCIETY},

abstract = {Abstract 

 In a representative vignette study in Germany with 1,653 respondents, we investigated laypeople’s attribution of moral responsibility in collaborative medical diagnosis. Specifically, we compare people’s judgments in a setting in which physicians are supported by an AI-based recommender system to a setting in which they are supported by a human colleague. It turns out that people tend to attribute moral responsibility to the artificial agent, although this is traditionally considered a category mistake in normative ethics. This tendency is stronger when people believe that AI may become conscious at some point. In consequence, less responsibility is attributed to human agents in settings with hybrid diagnostic teams than in settings with human-only diagnostic teams. Our findings may have implications for behavior exhibited in contexts of collaborative medical decision making with AI-based as opposed to human recommenders because less responsibility is attributed to agents who have the mental capacity to care about outcomes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{frenken_feasibility_2024,

title = {Feasibility of Optical Biopsy During Endoscopic Sinus Surgery With Confocal Laser Endomicroscopy: A Pilot Study},

author = {Ann‐Kathrin Frenken and Matti Sievert and Bharat Panuganti and Marc Aubreville and Till Meyer and Agmal Scherzad and Thomas Gehrke and Matthias Scheich and Stephan Hackenberg and Miguel Goncalves},

url = {https://onlinelibrary.wiley.com/doi/10.1002/lary.31503},

doi = {10.1002/lary.31503},

issn = {0023-852X, 1531-4995},

year  = {2024},

date = {2024-05-01},

urldate = {2024-05-19},

journal = {The Laryngoscope},

pages = {lary.31503},

abstract = {Objective 

 Confocal laser endomicroscopy (CLE) is an optical imaging technique that allows in vivo, real‐time, microscope‐like assessment of superficial lesions. Although there is substantial data on CLE use in the upper GI tract, there is limited information regarding its application in the nasal cavity and paranasal sinuses. This study aims to assess the feasibility and diagnostic metrics of CLE in the nasal cavity and paranasal sinuses regarding differentiation between healthy/benign and malignant tissue. These structures show, however, a wider variety of frequent and concomitant benign and malignant pathologies, which could pose an increased challenge for optical biopsy by CLE. 

 Methods 

 We performed CLE on a case series of six patients with various findings in the nose (three chronic rhinosinusitis, adenocarcinoma, meningoenzephalozele, esthesionneuroblastoma). Forty‐two sequences (3792 images) from various structures in the nasal cavity and/or paranasal sinuses were acquired. Biopsies were taken at corresponding locations and analyzed in hematoxylin and eosin staining as a standard of reference. Three independent examiners blinded to the histopathology assessed the sequences. 

 Results 

 Healthy and inflamed mucosa could be distinguished from malignant lesions with an accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 84.1%, 85.4%, 83.1%, 72.5%, and 92.1%, respectively, with a substantial agreement between raters (Fleiss 

 κ = 0.62). 

 Conclusion 

 This technique shows, despite its limitations, potential as an adjunctive imaging technique during sinus surgery; however, the creation of a scoring system based on reproducible and defined characteristics in a larger more diverse population should be the focus of further research to improve its diagnostic value and clinical utility. 

 Level of Evidence 

 NA 

 Laryngoscope 

 , 2024},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{krugel_perceived_2024-1,

title = {Perceived responsibility in AI-supported medicine},

author = {Sebastian Krügel and Jonas Ammeling and Marc Aubreville and Alexis Fritz and Angelika Kießig and Matthias Uhl},

url = {https://link.springer.com/10.1007/s00146-024-01972-6},

doi = {10.1007/s00146-024-01972-6},

issn = {0951-5666, 1435-5655},

year  = {2024},

date = {2024-05-01},

urldate = {2024-05-01},

journal = {AI & SOCIETY},

abstract = {Abstract 

 In a representative vignette study in Germany with 1,653 respondents, we investigated laypeople’s attribution of moral responsibility in collaborative medical diagnosis. Specifically, we compare people’s judgments in a setting in which physicians are supported by an AI-based recommender system to a setting in which they are supported by a human colleague. It turns out that people tend to attribute moral responsibility to the artificial agent, although this is traditionally considered a category mistake in normative ethics. This tendency is stronger when people believe that AI may become conscious at some point. In consequence, less responsibility is attributed to human agents in settings with hybrid diagnostic teams than in settings with human-only diagnostic teams. Our findings may have implications for behavior exhibited in contexts of collaborative medical decision making with AI-based as opposed to human recommenders because less responsibility is attributed to agents who have the mental capacity to care about outcomes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{oetter_oral_2024,

title = {Oral mucosa - an examination map for confocal laser endomicroscopy within the oral cavity: an experimental clinical study},

author = {Nicolai Oetter and Jonas Pröll and Matti Sievert and Miguel Goncalves and Maximilian Rohde and Christopher-Philipp Nobis and Christian Knipfer and Marc Aubreville and Zhaoya Pan and Katharina Breininger and Andreas Maier and Marco Kesting and Florian Stelzle},

url = {https://link.springer.com/10.1007/s00784-024-05664-9},

doi = {10.1007/s00784-024-05664-9},

issn = {1436-3771},

year  = {2024},

date = {2024-04-01},

urldate = {2024-04-24},

journal = {Clinical Oral Investigations},

volume = {28},

number = {5},

pages = {266},

abstract = {Abstract 

 Objectives 

 Confocal laser endomicroscopy (CLE) is an optical method that enables microscopic visualization of oral mucosa. Previous studies have shown that it is possible to differentiate between physiological and malignant oral mucosa. However, differences in mucosal architecture were not taken into account. The objective was to map the different oral mucosal morphologies and to establish a “CLE map” of physiological mucosa as baseline for further application of this powerful technology. 

 Materials and methods 

 The CLE database consisted of 27 patients. The following spots were examined: (1) upper lip (intraoral) (2) alveolar ridge (3) lateral tongue (4) floor of the mouth (5) hard palate (6) intercalary line. All sequences were examined by two CLE experts for morphological differences and video quality. 

 Results 

 Analysis revealed clear differences in image quality and possibility of depicting tissue morphologies between the various localizations of oral mucosa: imaging of the alveolar ridge and hard palate showed visually most discriminative tissue morphology. Labial mucosa was also visualized well using CLE. Here, typical morphological features such as uniform cells with regular intercellular gaps and vessels could be clearly depicted. Image generation and evaluation was particularly difficult in the area of the buccal mucosa, the lateral tongue and the floor of the mouth. 

 Conclusion 

 A physiological “CLE map” for the entire oral cavity could be created for the first time. 

 Clinical relevance 

 This will make it possible to take into account the existing physiological morphological features when differentiating between normal mucosa and oral squamous cell carcinoma in future work.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sievert_diagnosis_2024,

title = {Diagnosis of malignancy in oropharyngeal confocal laser endomicroscopy using GPT 4.0 with vision},

author = {Matti Sievert and Marc Aubreville and Sarina Katrin Mueller and Markus Eckstein and Katharina Breininger and Heinrich Iro and Miguel Goncalves},

url = {https://link.springer.com/10.1007/s00405-024-08476-5},

doi = {10.1007/s00405-024-08476-5},

issn = {0937-4477, 1434-4726},

year  = {2024},

date = {2024-02-01},

urldate = {2024-02-21},

journal = {European Archives of Oto-Rhino-Laryngology},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{aubreville_developing_2024,

title = {Developing Robust AI Applications for Clinical Use: The Special Case of Pathology},

author = {Marc Aubreville},

doi = {https://doi.org/10.47184/tp.2024.01.04},

year  = {2024},

date = {2024-01-01},

journal = {Trillium Pathology},

volume = {TP 1/2004},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fragoso-garcia_automated_2023,

title = {Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images},

author = {Marco Fragoso-Garcia and Frauke Wilm and Christof A. Bertram and Sophie Merz and Anja Schmidt and Taryn Donovan and Andrea Fuchs-Baumgartinger and Alexander Bartel and Christian Marzahl and Laura Diehl and Chloe Puget and Andreas Maier and Marc Aubreville and Katharina Breininger and Robert Klopfleisch},

url = {http://journals.sagepub.com/doi/10.1177/03009858231189205},

doi = {10.1177/03009858231189205},

issn = {0300-9858, 1544-2217},

year  = {2023},

date = {2023-07-01},

urldate = {2023-08-07},

journal = {Veterinary Pathology},

pages = {03009858231189205},

abstract = {Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hirling_segmentation_2023,

title = {Segmentation metric misinterpretations in bioimage analysis},

author = {Dominik Hirling and Ervin Tasnadi and Juan Caicedo and Maria V. Caroprese and Rickard Sjögren and Marc Aubreville and Krisztian Koos and Peter Horvath},

url = {https://www.nature.com/articles/s41592-023-01942-8},

doi = {10.1038/s41592-023-01942-8},

issn = {1548-7091, 1548-7105},

year  = {2023},

date = {2023-07-01},

urldate = {2023-07-28},

journal = {Nature Methods},

abstract = {Abstract 

 Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often misinterpreted and multiple definitions coexist with the same name. Here we present the ambiguities of evaluation metrics for segmentation algorithms and show how these misinterpretations can alter leaderboards of influential competitions. We also propose guidelines for how the currently existing problems could be tackled.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {},

pubstate = {published},

tppubtype = {article}

}

@article{wilm_pan-tumor_2023,

title = {Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry},

author = {Frauke Wilm and Christian Ihling and Gábor Méhes and Luigi Terracciano and Chloé Puget and Robert Klopfleisch and Peter Schüffler and Marc Aubreville and Andreas Maier and Thomas Mrowiec and Katharina Breininger},

url = {https://linkinghub.elsevier.com/retrieve/pii/S2153353923001153},

doi = {10.1016/j.jpi.2023.100301},

issn = {21533539},

year  = {2023},

date = {2023-02-01},

urldate = {2023-07-01},

journal = {Journal of Pathology Informatics},

volume = {14},

pages = {100301},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{aubreville_mitosis_2023,

title = {Mitosis domain generalization in histopathology images — The MIDOG challenge},

author = {Marc Aubreville and Nikolas Stathonikos and Christof A. Bertram and Robert Klopfleisch and Natalie Ter Hoeve and Francesco Ciompi and Frauke Wilm and Christian Marzahl and Taryn A. Donovan and Andreas Maier and Jack Breen and Nishant Ravikumar and Youjin Chung and Jinah Park and Ramin Nateghi and Fattaneh Pourakpour and Rutger H. J. Fick and Saima Ben Hadj and Mostafa Jahanifar and Adam Shephard and Jakob Dexl and Thomas Wittenberg and Satoshi Kondo and Maxime W. Lafarge and Viktor H. Koelzer and Jingtang Liang and Yubo Wang and Xi Long and Jingxin Liu and Salar Razavi and April Khademi and Sen Yang and Xiyue Wang and Ramona Erber and Andrea Klang and Karoline Lipnik and Pompei Bolfa and Michael J. Dark and Gabriel Wasinger and Mitko Veta and Katharina Breininger},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1361841522003279},

doi = {10.1016/j.media.2022.102699},

issn = {13618415},

year  = {2023},

date = {2023-01-01},

urldate = {2023-05-19},

journal = {Medical Image Analysis},

volume = {84},

pages = {102699},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pan_defining_2023,

title = {Defining a baseline identification of artifacts in confocal laser endomicroscopy in head and neck cancer imaging},

author = {Zhaoya Pan and Katharina Breininger and Marc Aubreville and Florian Stelzle and Nicolai Oetter and Andreas Maier and Konstantinos Mantsopoulos and Heinrich Iro and Miguel Goncalves and Matti Sievert},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0196070922004069},

doi = {10.1016/j.amjoto.2022.103779},

issn = {01960709},

year  = {2023},

date = {2023-01-01},

urldate = {2023-07-01},

journal = {American Journal of Otolaryngology},

volume = {44},

number = {2},

pages = {103779},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sievert_validation_2022,

title = {Validation of a classification and scoring system for the diagnosis of laryngeal and pharyngeal squamous cell carcinomas by confocal laser endomicroscopy},

author = {Matti Sievert and Konstantinos Mantsopoulos and Sarina K. Mueller and Robin Rupp and Markus Eckstein and Florian Stelzle and Nicolai Oetter and Andreas Maier and Marc Aubreville and Heinrich Iro and Miguel Goncalves},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1808869421001245},

doi = {10.1016/j.bjorl.2021.06.002},

issn = {18088694},

year  = {2022},

date = {2022-11-01},

urldate = {2023-06-30},

journal = {Brazilian Journal of Otorhinolaryngology},

volume = {88},

pages = {S26–S32},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{wilm_pan-tumor_2022,

title = {Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset},

author = {Frauke Wilm and Marco Fragoso and Christian Marzahl and Jingna Qiu and Chloé Puget and Laura Diehl and Christof A. Bertram and Robert Klopfleisch and Andreas Maier and Katharina Breininger and Marc Aubreville},

url = {https://www.nature.com/articles/s41597-022-01692-w},

doi = {10.1038/s41597-022-01692-w},

issn = {2052-4463},

year  = {2022},

date = {2022-09-01},

urldate = {2023-06-30},

journal = {Scientific Data},

volume = {9},

number = {1},

pages = {588},

abstract = {Abstract 

 Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{marzahl_inter-species_2022,

title = {Inter-species cell detection - datasets on pulmonary hemosiderophages in equine, human and feline specimens},

author = {Christian Marzahl and Jenny Hill and Jason Stayt and Dorothee Bienzle and Lutz Welker and Frauke Wilm and Jörn Voigt and Marc Aubreville and Andreas Maier and Robert Klopfleisch and Katharina Breininger and Christof A. Bertram},

url = {https://www.nature.com/articles/s41597-022-01389-0},

doi = {10.1038/s41597-022-01389-0},

issn = {2052-4463},

year  = {2022},

date = {2022-06-01},

urldate = {2023-07-01},

journal = {Scientific Data},

volume = {9},

number = {1},

pages = {269},

abstract = {Abstract 

 Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolar lavage fluid (BALF) using a 5-tier scoring system of alveolar macrophages based on their hemosiderin content is considered the most sensitive diagnostic method. We introduce a novel, fully annotated multi-species P-Hem dataset, which consists of 74 cytology whole slide images (WSIs) with equine, feline and human samples. To create this high-quality and high-quantity dataset, we developed an annotation pipeline combining human expertise with deep learning and data visualisation techniques. We applied a deep learning-based object detection approach trained on 17 expertly annotated equine WSIs, to the remaining 39 equine, 12 human and 7 feline WSIs. The resulting annotations were semi-automatically screened for errors on multiple types of specialised annotation maps and finally reviewed by a trained pathologist. Our dataset contains a total of 297,383 hemosiderophages classified into five grades. It is one of the largest publicly available WSIs datasets with respect to the number of annotations, the scanned area and the number of species covered.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sievert_impact_2022,

title = {Impact of intraepithelial capillary loops and atypical vessels in confocal laser endomicroscopy for the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma},

author = {Matti Sievert and Markus Eckstein and Konstantinos Mantsopoulos and Sarina K. Mueller and Florian Stelzle and Marc Aubreville and Nicolai Oetter and Andreas Maier and Heinrich Iro and Miguel Goncalves},

url = {https://link.springer.com/10.1007/s00405-021-06954-8},

doi = {10.1007/s00405-021-06954-8},

issn = {0937-4477, 1434-4726},

year  = {2022},

date = {2022-04-01},

urldate = {2023-06-30},

journal = {European Archives of Oto-Rhino-Laryngology},

volume = {279},

number = {4},

pages = {2029–2037},

abstract = {Abstract 

 Purpose 

 Confocal laser endomicroscopy (CLE) allows surface imaging of the laryngeal and pharyngeal mucosa in vivo at a thousand-fold magnification. This study aims to compare irregular blood vessels and intraepithelial capillary loops in healthy mucosa and squamous cell carcinoma (SCC) via CLE. 

 Materials and methods 

 We included ten patients with confirmed SCC and planned total laryngectomy in this study between March 2020 and February 2021. CLE images of these patients were collected and compared with the corresponding histology in hematoxylin and eosin staining. We analyzed the characteristic endomicroscopic patterns of blood vessels and intraepithelial capillary loops for the diagnosis of SCC. 

 Results 

 In a total of 54 sequences, we identified 243 blood vessels which were analyzed regarding structure, diameter, and Fluorescein leakage, confirming that irregular, corkscrew-like vessels (24.4% vs. 1.3%; 

 P 

 < .001), dilated intraepithelial capillary loops (90.8% vs. 28.7%; 

 P 

 < .001), and increased capillary leakage (40.7% vs. 2.5%; 

 P 

 < .001), are significantly more frequently detected in SCC compared to the healthy epithelium. We defined a vessel diameter of 30 μm in capillary loops as a cut-off value, obtaining a sensitivity, specificity, PPV, and NPV and accuracy of 90.6%, 71.3%, 57.4%, 94.7%, and 77.1%, respectively, for the detection of malignancy based solely on capillary architecture. 

 Conclusion 

 Capillaries within malignant lesions are fundamentally different from those in healthy mucosa regions. The capillary architecture is a significant feature aiding the identification of malignant mucosa areas during in-vivo, real-time CLE examination.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bertram_computer-assisted_2022,

title = {Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy},

author = {Christof A. Bertram and Marc Aubreville and Taryn A. Donovan and Alexander Bartel and Frauke Wilm and Christian Marzahl and Charles-Antoine Assenmacher and Kathrin Becker and Mark Bennett and Sarah Corner and Brieuc Cossic and Daniela Denk and Martina Dettwiler and Beatriz Garcia Gonzalez and Corinne Gurtner and Ann-Kathrin Haverkamp and Annabelle Heier and Annika Lehmbecker and Sophie Merz and Erica L. Noland and Stephanie Plog and Anja Schmidt and Franziska Sebastian and Dodd G. Sledge and Rebecca C. Smedley and Marco Tecilla and Tuddow Thaiwong and Andrea Fuchs-Baumgartinger and Donald J. Meuten and Katharina Breininger and Matti Kiupel and Andreas Maier and Robert Klopfleisch},

url = {http://journals.sagepub.com/doi/10.1177/03009858211067478},

doi = {10.1177/03009858211067478},

issn = {0300-9858, 1544-2217},

year  = {2022},

date = {2022-03-01},

urldate = {2023-06-30},

journal = {Veterinary Pathology},

volume = {59},

number = {2},

pages = {211–226},

abstract = {The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sievert_intraoperative_2021,

title = {Intraoperative free margins assessment of oropharyngeal squamous cell carcinoma with confocal laser endomicroscopy: a pilot study},

author = {Matti Sievert and Florian Stelzle and Marc Aubreville and Sarina K. Mueller and Markus Eckstein and Nicolai Oetter and Andreas Maier and Konstantinos Mantsopoulos and Heinrich Iro and Miguel Goncalves},

url = {https://link.springer.com/10.1007/s00405-021-06659-y},

doi = {10.1007/s00405-021-06659-y},

issn = {0937-4477, 1434-4726},

year  = {2021},

date = {2021-11-01},

urldate = {2023-06-30},

journal = {European Archives of Oto-Rhino-Laryngology},

volume = {278},

number = {11},

pages = {4433–4439},

abstract = {Abstract 

 Purpose 

 This pilot study aimed to assess the feasibility of intraoperative assessment of safe margins with confocal laser endomicroscopy (CLE) during oropharyngeal squamous cell carcinoma (OPSCC) surgery. 

 Methods 

 We included five consecutive patients confirmed OPSCC and planned tumor resection in September and October 2020. Healthy appearing mucosa in the marginal zone, and the tumor margin, were examined with CLE and biopsy during tumor resection. A total of 12,809 CLE frames were correlated with the gold standard of hematoxylin and eosin staining. Three head and neck surgeons and one pathologist were asked to identify carcinoma in a sample of 169 representative images, blinded to the histological results. 

 Results 

 Healthy mucosa showed epithelium with uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. CLE optical biopsy of OPSCC demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, PPV, and NPV of 86%, 90%, 79%, 88%, and 82%, respectively, with inter-rater reliability and 

 κ 

 -value of 0.60. 

 Conclusion 

 CLE can be easily integrated into the intraoperative setting, generate real-time, in-vivo microscopic images of the oropharynx for evaluation and demarcation of cancer. It can eventually contribute to a less radical approach by enabling a more precise evaluation of the cancer margin.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{meuten_international_2021,

title = {International Guidelines for Veterinary Tumor Pathology: A Call to Action},

author = {Donald J. Meuten and Frances M. Moore and Taryn A. Donovan and Christof A. Bertram and Robert Klopfleisch and Robert A. Foster and Rebecca C. Smedley and Michael J. Dark and Milan Milovancev and Paul Stromberg and Bruce H. Williams and Marc Aubreville and Giancarlo Avallone and Pompei Bolfa and John Cullen and Michelle M. Dennis and Michael Goldschmidt and Richard Luong and Andrew D. Miller and Margaret A. Miller and John S. Munday and Paola Roccabianca and Elisa N. Salas and F. Yvonne Schulman and Renee Laufer-Amorim and Midori G. Asakawa and Linden Craig and Nick Dervisis and D. Glen Esplin and Jeanne W. George and Marlene Hauck and Yumiko Kagawa and Matti Kiupel and Keith Linder and Kristina Meichner and Laura Marconato and Michelle L. Oblak and Renato L. Santos and R. Mark Simpson and Harold Tvedten and Derick Whitley},

url = {http://journals.sagepub.com/doi/10.1177/03009858211013712},

doi = {10.1177/03009858211013712},

issn = {0300-9858, 1544-2217},

year  = {2021},

date = {2021-09-01},

urldate = {2023-06-30},

journal = {Veterinary Pathology},

volume = {58},

number = {5},

pages = {766–794},

abstract = {Standardization of tumor assessment lays the foundation for validation of grading systems, permits reproducibility of oncologic studies among investigators, and increases confidence in the significance of study results. Currently, there is minimal methodological standardization for assessing tumors in veterinary medicine, with few attempts to validate published protocols and grading schemes. The current article attempts to address these shortcomings by providing standard guidelines for tumor assessment parameters and protocols for evaluating specific tumor types. More detailed information is available in the Supplemental Files, the intention of which is 2-fold: publication as part of this commentary, but more importantly, these will be available as “living documents” on a website ( www.vetcancerprotocols.org ), which will be updated as new information is presented in the peer-reviewed literature. Our hope is that veterinary pathologists will agree that this initiative is needed, and will contribute to and utilize this information for routine diagnostic work and oncologic studies. Journal editors and reviewers can utilize checklists to ensure publications include sufficient detail and standardized methods of tumor assessment. To maintain the relevance of the guidelines and protocols, it is critical that the information is periodically updated and revised as new studies are published and validated with the intent of providing a repository of this information. Our hope is that this initiative (a continuation of efforts published in this journal in 2011) will facilitate collaboration and reproducibility between pathologists and institutions, increase case numbers, and strengthen clinical research findings, thus ensuring continued progress in veterinary oncologic pathology and improving patient care.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sievert_feasibility_2021,

title = {Feasibility of intraoperative assessment of safe surgical margins during laryngectomy with confocal laser endomicroscopy: A pilot study},

author = {Matti Sievert and Nicolai Oetter and Marc Aubreville and Florian Stelzle and Andreas Maier and Markus Eckstein and Konstantinos Mantsopoulos and Antoniu-Oreste Gostian and Sarina K Mueller and Michael Koch and Abbas Agaimy and Heinrich Iro and Miguel Goncalves},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0385814621000237},

doi = {10.1016/j.anl.2021.01.005},

issn = {03858146},

year  = {2021},

date = {2021-08-01},

urldate = {2023-06-30},

journal = {Auris Nasus Larynx},

volume = {48},

number = {4},

pages = {764–769},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{donovan_mitotic_2021,

title = {Mitotic Figures—Normal, Atypical, and Imposters: A Guide to Identification},

author = {Taryn A. Donovan and Frances M. Moore and Christof A. Bertram and Richard Luong and Pompei Bolfa and Robert Klopfleisch and Harold Tvedten and Elisa N. Salas and Derick B. Whitley and Marc Aubreville and Donald J. Meuten},

url = {http://journals.sagepub.com/doi/10.1177/0300985820980049},

doi = {10.1177/0300985820980049},

issn = {0300-9858, 1544-2217},

year  = {2021},

date = {2021-03-01},

urldate = {2023-06-30},

journal = {Veterinary Pathology},

volume = {58},

number = {2},

pages = {243–257},

abstract = {Counting mitotic figures (MF) in hematoxylin and eosin–stained histologic sections is an integral part of the diagnostic pathologist’s tumor evaluation. The mitotic count (MC) is used alone or as part of a grading scheme for assessment of prognosis and clinical decisions. Determining MCs is subjective, somewhat laborious, and has interobserver variation. Proposals for standardizing this parameter in the veterinary field are limited to terminology (use of the term MC) and area (MC is counted in an area measuring 2.37 mm 

 2 

 ). Digital imaging techniques are now commonplace and widely used among veterinary pathologists, and field of view area can be easily calculated with digital imaging software. In addition to standardizing the methods of counting MF, the morphologic characteristics of MF and distinguishing atypical mitotic figures (AMF) versus mitotic-like figures (MLF) need to be defined. This article provides morphologic criteria for MF identification and for distinguishing normal phases of MF from AMF and MLF. Pertinent features of digital microscopy and application of computational pathology (CPATH) methods are discussed. Correct identification of MF will improve MC consistency, reproducibility, and accuracy obtained from manual (glass slide or whole-slide imaging) and CPATH approaches.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{marzahl_exact_2021,

title = {EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control},

author = {Christian Marzahl and Marc Aubreville and Christof A. Bertram and Jennifer Maier and Christian Bergler and Christine Kröger and Jörn Voigt and Katharina Breininger and Robert Klopfleisch and Andreas Maier},

url = {https://www.nature.com/articles/s41598-021-83827-4},

doi = {10.1038/s41598-021-83827-4},

issn = {2045-2322},

year  = {2021},

date = {2021-02-01},

urldate = {2023-06-30},

journal = {Scientific Reports},

volume = {11},

number = {1},

pages = {4343},

abstract = {Abstract 

 In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation. However, keeping track of these annotations to ensure a high-quality multi-purpose data set is a challenging and labour intensive task. We developed the open-source online platform EXACT (EXpert Algorithm Collaboration Tool) that enables the collaborative interdisciplinary analysis of images from different domains online and offline. EXACT supports multi-gigapixel medical whole slide images as well as image series with thousands of images. The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies. This is combined with a version control system which makes it possible to keep track of changes in the data sets and, for example, to link the results of deep learning experiments to specific data set versions. EXACT is freely available and has already been successfully applied to a broad range of annotation tasks, including highly diverse applications like deep learning supported cytology scoring, interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}