Výsledky vyhledávání - "Fragoso, Marco"

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Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset

Autor: Wilm, Frauke, Fragoso, Marco, Bertram, Christof A., Stathonikos, Nikolas, Öttl, Mathias, Qiu, Jingna, Klopfleisch, Robert, Maier, Andreas, Breininger, Katharina, Aubreville, Marc

In histopathology, scanner-induced domain shifts are known to impede the performance of trained neural networks when tested on unseen data. Multi-domain pre-training or dedicated domain-generalization techniques can help to develop domain-agnostic al

Externí odkaz: http://arxiv.org/abs/2301.04423

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Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology

Autor: Wilm, Frauke, Fragoso, Marco, Bertram, Christof A., Stathonikos, Nikolas, Öttl, Mathias, Qiu, Jingna, Klopfleisch, Robert, Maier, Andreas, Aubreville, Marc, Breininger, Katharina

Computer-aided systems in histopathology are often challenged by various sources of domain shift that impact the performance of these algorithms considerably. We investigated the potential of using self-supervised pre-training to overcome scanner-ind

Externí odkaz: http://arxiv.org/abs/2211.16141

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Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

Autor: Wilm, Frauke, Fragoso, Marco, Marzahl, Christian, Qiu, Jingna, Puget, Chloé, Diehl, Laura, Bertram, Christof A., Klopfleisch, Robert, Maier, Andreas, Breininger, Katharina, Aubreville, Marc

Publikováno v: Scientific Data vol. 9 (2022)

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 re

Externí odkaz: http://arxiv.org/abs/2201.11446

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Dataset on Bi- and Multi-Nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors

Autor: Bertram, Christof A., Donovan, Taryn A., Tecilla, Marco, Bartenschlager, Florian, Fragoso, Marco, Wilm, Frauke, Marzahl, Christian, Breininger, Katharina, Maier, Andreas, Klopfleisch, Robert, Aubreville, Marc

Tumor cells with two nuclei (binucleated cells, BiNC) or more nuclei (multinucleated cells, MuNC) indicate an increased amount of cellular genetic material which is thought to facilitate oncogenesis, tumor progression and treatment resistance. In can

Externí odkaz: http://arxiv.org/abs/2101.01445

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Are fast labeling methods reliable? A case study of computer-aided expert annotations on microscopy slides

Autor: Marzahl, Christian, Bertram, Christof A., Aubreville, Marc, Petrick, Anne, Weiler, Kristina, Gläsel, Agnes C., Fragoso, Marco, Merz, Sophie, Bartenschlager, Florian, Hoppe, Judith, Langenhagen, Alina, Jasensky, Anne, Voigt, Jörn, Klopfleisch, Robert, Maier, Andreas

Deep-learning-based pipelines have shown the potential to revolutionalize microscopy image diagnostics by providing visual augmentations to a trained pathology expert. However, to match human performance, the methods rely on the availability of vast

Externí odkaz: http://arxiv.org/abs/2004.05838

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Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region

Autor: Aubreville, Marc, Bertram, Christof A., Marzahl, Christian, Gurtner, Corinne, Dettwiler, Martina, Schmidt, Anja, Bartenschlager, Florian, Merz, Sophie, Fragoso, Marco, Kershaw, Olivia, Klopfleisch, Robert, Maier, Andreas

Publikováno v: Sci Rep. 2020 Oct 5;10(1):16447

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 di

Externí odkaz: http://arxiv.org/abs/1902.05414

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