Publications
Here you can find our latest publications. We are working on deep learning-based microscopy applications, such as pathology:
2018
Aubreville, Marc; Bertram, Christof; Klopfleisch, Robert; Maier, Andreas
SlideRunner: A Tool for Massive Cell Annotations in Whole Slide Images Book Section
In: Maier, Andreas; Deserno, Thomas M.; Handels, Heinz; Maier-Hein, Klaus Hermann; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2018, pp. 309–314, Springer Berlin Heidelberg, Berlin, Heidelberg, 2018, ISBN: 978-3-662-56536-0 978-3-662-56537-7, (Series Title: Informatik aktuell).
@incollection{maier_sliderunner_2018,
title = {SlideRunner: A Tool for Massive Cell Annotations in Whole Slide Images},
author = {Marc Aubreville and Christof Bertram and Robert Klopfleisch and Andreas Maier},
editor = {Andreas Maier and Thomas M. Deserno and Heinz Handels and Klaus Hermann Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {http://link.springer.com/10.1007/978-3-662-56537-7_81},
doi = {10.1007/978-3-662-56537-7_81},
isbn = {978-3-662-56536-0 978-3-662-56537-7},
year = {2018},
date = {2018-01-01},
urldate = {2025-02-11},
booktitle = {Bildverarbeitung für die Medizin 2018},
pages = {309–314},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Aubreville, Marc; Goncalves, Miguel; Knipfer, Christian; Oetter, Nicolai; Würfl, Tobias; Neumann, Helmut; Stelzle, Florian; Bohr, Christopher; Maier, Andreas
Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images – A Cross-site Robustness Assessment: Proceedings Article
In: Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, pp. 27–34, SCITEPRESS – Science and Technology Publications, Funchal, Madeira, Portugal, 2018, ISBN: 978-989-758-307-0.
@inproceedings{aubreville_patch-based_2018,
title = {Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images - A Cross-site Robustness Assessment:},
author = {Marc Aubreville and Miguel Goncalves and Christian Knipfer and Nicolai Oetter and Tobias Würfl and Helmut Neumann and Florian Stelzle and Christopher Bohr and Andreas Maier},
url = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006534700270034},
doi = {10.5220/0006534700270034},
isbn = {978-989-758-307-0},
year = {2018},
date = {2018-01-01},
urldate = {2025-02-11},
booktitle = {Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies},
pages = {27–34},
publisher = {SCITEPRESS - Science and Technology Publications},
address = {Funchal, Madeira, Portugal},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2017
Aubreville, Marc; Knipfer, Christian; Oetter, Nicolai; Jaremenko, Christian; Rodner, Erik; Denzler, Joachim; Bohr, Christopher; Neumann, Helmut; Stelzle, Florian; Maier, Andreas
Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning Journal Article
In: Scientific Reports, vol. 7, no. 1, pp. 11979, 2017, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags:
@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}
}
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%).
Aubreville, Marc; Krappmann, Maximilian; Bertram, Christof; Klopfleisch, Robert; Maier, Andreas
A Guided Spatial Transformer Network for Histology Cell Differentiation Miscellaneous
2017, (Artwork Size: 5 pages ISBN: 9783038680369 ISSN: 2070-5786 Pages: 5 pages Publication Title: Eurographics Workshop on Visual Computing for Biology and Medicine).
Abstract | Links | BibTeX | Tags: Applied computing, Bioinformatics, Computing methodologies, FOS: Computer and information sciences, Neural networks, Object detection
@misc{aubreville_guided_2017,
title = {A Guided Spatial Transformer Network for Histology Cell Differentiation},
author = {Marc Aubreville and Maximilian Krappmann and Christof Bertram and Robert Klopfleisch and Andreas Maier},
url = {https://diglib.eg.org/handle/10.2312/vcbm20171233},
doi = {10.2312/VCBM.20171233},
year = {2017},
date = {2017-01-01},
urldate = {2025-02-11},
publisher = {The Eurographics Association},
abstract = {Identification and counting of cells and mitotic figures is a standard task in diagnostic histopathology. Due to the large overall cell count on histological slides and the potential sparse prevalence of some relevant cell types or mitotic figures, retrieving annotation data for sufficient statistics is a tedious task and prone to a significant error in assessment. Automatic classification and segmentation is a classic task in digital pathology, yet it is not solved to a sufficient degree. We present a novel approach for cell and mitotic figure classification, based on a deep convolutional network with an incorporated Spatial Transformer Network. The network was trained on a novel data set with ten thousand mitotic figures, about ten times more than previous data sets. The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells and granulocytes) and their position within an image. The mean accuracy of the algorithm in a five-fold cross-validation is 91.45 %. In our view, the approach is a promising step into the direction of a more objective and accurate, semi-automatized mitosis counting supporting the pathologist.},
note = {Artwork Size: 5 pages
ISBN: 9783038680369
ISSN: 2070-5786
Pages: 5 pages
Publication Title: Eurographics Workshop on Visual Computing for Biology and Medicine},
keywords = {Applied computing, Bioinformatics, Computing methodologies, FOS: Computer and information sciences, Neural networks, Object detection},
pubstate = {published},
tppubtype = {misc}
}