@article{aubreville_completely_2020,

title = {A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research},

author = {Marc Aubreville and Christof A. Bertram and Taryn A. Donovan and Christian Marzahl and Andreas Maier and Robert Klopfleisch},

url = {https://www.nature.com/articles/s41597-020-00756-z},

doi = {10.1038/s41597-020-00756-z},

issn = {2052-4463},

year  = {2020},

date = {2020-11-01},

urldate = {2023-06-30},

journal = {Scientific Data},

volume = {7},

number = {1},

pages = {417},

abstract = {Abstract 

 Canine mammary carcinoma (CMC) has been used as a model to investigate the pathogenesis of human breast cancer and the same grading scheme is commonly used to assess tumor malignancy in both. One key component of this grading scheme is the density of mitotic figures (MF). Current publicly available datasets on human breast cancer only provide annotations for small subsets of whole slide images (WSIs). We present a novel dataset of 21 WSIs of CMC completely annotated for MF. For this, a pathologist screened all WSIs for potential MF and structures with a similar appearance. A second expert blindly assigned labels, and for non-matching labels, a third expert assigned the final labels. Additionally, we used machine learning to identify previously undetected MF. Finally, we performed representation learning and two-dimensional projection to further increase the consistency of the annotations. Our dataset consists of 13,907 MF and 36,379 hard negatives. We achieved a mean F1-score of 0.791 on the test set and of up to 0.696 on a human breast cancer dataset.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{aubreville_deep_2020,

title = {Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region},

author = {Marc Aubreville and Christof A. Bertram and Christian Marzahl and Corinne Gurtner and Martina Dettwiler and Anja Schmidt and Florian Bartenschlager and Sophie Merz and Marco Fragoso and Olivia Kershaw and Robert Klopfleisch and Andreas Maier},

url = {https://www.nature.com/articles/s41598-020-73246-2},

doi = {10.1038/s41598-020-73246-2},

issn = {2045-2322},

year  = {2020},

date = {2020-10-01},

urldate = {2023-06-30},

journal = {Scientific Reports},

volume = {10},

number = {1},

pages = {16447},

abstract = {Abstract 

 Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution in the tumor section. We aimed to assess the question, how significantly the area selection could impact the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked eight veterinary pathologists (five board-certified, three in training) to select a field of interest for the mitotic count. To assess the potential difference on the mitotic count, we compared the mitotic count of the selected regions to the overall distribution on the slide. Additionally, we evaluated three deep learning-based methods for the assessment of highest mitotic density: In one approach, the model would directly try to predict the mitotic count for the presented image patches as a regression task. The second method aims at deriving a segmentation mask for mitotic figures, which is then used to obtain a mitotic density. Finally, we evaluated a two-stage object-detection pipeline based on state-of-the-art architectures to identify individual mitotic figures. We found that the predictions by all models were, on average, better than those of the experts. The two-stage object detector performed best and outperformed most of the human pathologists on the majority of tumor cases. The correlation between the predicted and the ground truth mitotic count was also best for this approach (0.963–0.979). Further, we found considerable differences in position selection between pathologists, which could partially explain the high variance that has been reported for the manual mitotic count. To achieve better inter-rater agreement, we propose to use a computer-based area selection for support of the pathologist in the manual mitotic count.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{marzahl_deep_2020,

title = {Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides},

author = {Christian Marzahl and Marc Aubreville and Christof A. Bertram and Jason Stayt and Anne-Katherine Jasensky and Florian Bartenschlager and Marco Fragoso-Garcia and Ann K. Barton and Svenja Elsemann and Samir Jabari and Jens Krauth and Prathmesh Madhu and Jörn Voigt and Jenny Hill and Robert Klopfleisch and Andreas Maier},

url = {https://www.nature.com/articles/s41598-020-65958-2},

doi = {10.1038/s41598-020-65958-2},

issn = {2045-2322},

year  = {2020},

date = {2020-08-01},

urldate = {2023-06-30},

journal = {Scientific Reports},

volume = {10},

number = {1},

pages = {9795},

abstract = {Abstract Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophages are classified depending on the degree of cytoplasmic hemosiderin content. The current gold standard is manual grading, which is however monotonous and time-consuming. We evaluated state-of-the-art deep learning-based methods for single cell macrophage classification and compared them against the performance of nine cytology experts and evaluated inter- and intra-observer variability. Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78,047 hemosiderophages. Our deep learning-based approach reached a concordance of 0.85, partially exceeding human expert concordance (0.68 to 0.86, mean of 0.73, SD of 0.04). Intra-observer variability was high (0.68 to 0.88) and inter-observer concordance was moderate (Fleiss’ kappa = 0.67). Our object detection approach has a mean average precision of 0.66 over the five classes from the whole slide gigapixel image and a computation time of below two minutes. To mitigate the high inter- and intra-rater variability, we propose our automated object detection pipeline, enabling accurate, reproducible and quick EIPH scoring in WSI.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bertram_computerized_2020,

title = {Computerized Calculation of Mitotic Count Distribution in Canine Cutaneous Mast Cell Tumor Sections: Mitotic Count Is Area Dependent},

author = {Christof A. Bertram and Marc Aubreville and Corinne Gurtner and Alexander Bartel and Sarah M. Corner and Martina Dettwiler and Olivia Kershaw and Erica L. Noland and Anja Schmidt and Dodd G. Sledge and Rebecca C. Smedley and Tuddow Thaiwong and Matti Kiupel and Andreas Maier and Robert Klopfleisch},

url = {http://journals.sagepub.com/doi/10.1177/0300985819890686},

doi = {10.1177/0300985819890686},

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

year  = {2020},

date = {2020-03-01},

urldate = {2023-06-30},

journal = {Veterinary Pathology},

volume = {57},

number = {2},

pages = {214–226},

abstract = {Mitotic count (MC) is an important element for grading canine cutaneous mast cell tumors (ccMCTs) and is determined in 10 consecutive high-power fields with the highest mitotic activity. However, there is variability in area selection between pathologists. In this study, the MC distribution and the effect of area selection on the MC were analyzed in ccMCTs. Two pathologists independently annotated all mitotic figures in whole-slide images of 28 ccMCTs (ground truth). Automated image analysis was used to examine the ground truth distribution of the MC throughout the tumor section area, which was compared with the manual MCs of 11 pathologists. Computerized analysis demonstrated high variability of the MC within different tumor areas. There were 6 MCTs with consistently low MCs (MC<7 in all tumor areas), 13 cases with mostly high MCs (MC ≥7 in ≥75% of 10 high-power field areas), and 9 borderline cases with variable MCs around 7, which is a cutoff value for ccMCT grading. There was inconsistency among pathologists in identifying the areas with the highest density of mitotic figures throughout the 3 ccMCT groups; only 51.9% of the counts were consistent with the highest 25% of the ground truth MC distribution. Regardless, there was substantial agreement between pathologists in detecting tumors with MC ≥7. Falsely low MCs below 7 mainly occurred in 4 of 9 borderline cases that had very few ground truth areas with MC ≥7. The findings of this study highlight the need to further standardize how to select the region of the tumor in which to determine the MC.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bertram_mitotic_2020,

title = {Mitotic Count in Canine Cutaneous Mast Cell Tumours – Not Accurate but Reproducible},

author = {Christof A. Bertram and Marc Aubreville and Corinna Gurtner and Alexander Bartel and S. M. Corner and M. Dettwiler and O. Kershaw and E. L. Noland and A. Schmidt and D. G. Sledge and R. C. Smedley and T. Thaiwong and Matti Kiupel and A. Maier and Robert Klopfleisch},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0021997519303111},

doi = {10.1016/j.jcpa.2019.10.015},

issn = {00219975},

year  = {2020},

date = {2020-01-01},

urldate = {2023-06-30},

journal = {Journal of Comparative Pathology},

volume = {174},

pages = {143},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{goncalves_probe-based_2019,

title = {Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds},

author = {Miguel Goncalves and Marc Aubreville and Sarina K Mueller and Matti Sievert and Andreas Maier and Heinrich Iro and Christopher Bohr},

url = {https://www.actaitalica.it/article/view/117},

doi = {10.14639/0392-100X-2121},

issn = {1827-675X},

year  = {2019},

date = {2019-12-01},

urldate = {2023-06-30},

journal = {Acta Otorhinolaryngologica Italica},

volume = {39},

number = {6},

pages = {389–395},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bertram_large-scale_2019,

title = {A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor},

author = {Christof A. Bertram and Marc Aubreville and Christian Marzahl and Andreas Maier and Robert Klopfleisch},

url = {https://www.nature.com/articles/s41597-019-0290-4},

doi = {10.1038/s41597-019-0290-4},

issn = {2052-4463},

year  = {2019},

date = {2019-11-01},

urldate = {2023-06-30},

journal = {Scientific Data},

volume = {6},

number = {1},

pages = {274},

abstract = {Abstract 

 We introduce a novel, large-scale dataset for microscopy cell annotations. The dataset includes 32 whole slide images (WSI) of canine cutaneous mast cell tumors, selected to include both low grade cases as well as high grade cases. The slides have been completely annotated for mitotic figures and we provide secondary annotations for neoplastic mast cells, inflammatory granulocytes, and mitotic figure look-alikes. Additionally to a blinded two-expert manual annotation with consensus, we provide an algorithm-aided dataset, where potentially missed mitotic figures were detected by a deep neural network and subsequently assessed by two human experts. We included 262,481 annotations in total, out of which 44,880 represent mitotic figures. For algorithmic validation, we used a customized RetinaNet approach, followed by a cell classification network. We find F1-Scores of 0.786 and 0.820 for the manually labelled and the algorithm-aided dataset, respectively. The dataset provides, for the first time, WSIs completely annotated for mitotic figures and thus enables assessment of mitosis detection algorithms on complete WSIs as well as region of interest detection algorithms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{aubreville_deep_2019,

title = {Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images},

author = {Marc Aubreville and Maike Stoeve and Nicolai Oetter and Miguel Goncalves and Christian Knipfer and Helmut Neumann and Christopher Bohr and Florian Stelzle and Andreas Maier},

url = {http://link.springer.com/10.1007/s11548-018-1836-1},

doi = {10.1007/s11548-018-1836-1},

issn = {1861-6410, 1861-6429},

year  = {2019},

date = {2019-01-01},

urldate = {2023-06-30},

journal = {International Journal of Computer Assisted Radiology and Surgery},

volume = {14},

number = {1},

pages = {31–42},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{aubreville_automatic_2017,

title = {Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning},

author = {Marc Aubreville and Christian Knipfer and Nicolai Oetter and Christian Jaremenko and Erik Rodner and Joachim Denzler and Christopher Bohr and Helmut Neumann and Florian Stelzle and Andreas Maier},

url = {https://www.nature.com/articles/s41598-017-12320-8},

doi = {10.1038/s41598-017-12320-8},

issn = {2045-2322},

year  = {2017},

date = {2017-09-01},

urldate = {2023-06-30},

journal = {Scientific Reports},

volume = {7},

number = {1},

pages = {11979},

abstract = {Abstract 

 Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for 

 in vivo 

 cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC 

 in situ 

 . We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, specificity 90%).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}