Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Daniel Otero Baguer"'
Publikováno v:
Scientific Data, Vol 8, Iss 1, Pp 1-12 (2021)
Measurement(s) Low Dose Computed Tomography of the Chest • feature extraction objective Technology Type(s) digital curation • image processing technique Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the
Externí odkaz:
https://doaj.org/article/6a2f386a28994aa6a8db73f70a355873
Publikováno v:
Journal of Imaging, Vol 8, Iss 7, p 202 (2022)
In recent years, numerous deep-learning approaches have been developed for the analysis of histopathology Whole Slide Images (WSI). A recurrent issue is the lack of generalization ability of a model that has been trained with images of one laboratory
Externí odkaz:
https://doaj.org/article/54b8e27fa5e9433cac9bc5df14b3531f
Autor:
Jean Le’Clerc Arrastia, Nick Heilenkötter, Daniel Otero Baguer, Lena Hauberg-Lotte, Tobias Boskamp, Sonja Hetzer, Nicole Duschner, Jörg Schaller, Peter Maass
Publikováno v:
Journal of Imaging, Vol 7, Iss 4, p 71 (2021)
Accurate and fast assessment of resection margins is an essential part of a dermatopathologist’s clinical routine. In this work, we successfully develop a deep learning method to assist the dermatopathologists by marking critical regions that have
Externí odkaz:
https://doaj.org/article/4ce6abaf685c4ae9b692fe1cc3c512f4
Autor:
Philipp Jansen, Daniel Otero Baguer, Nicole Duschner, Jean Le’Clerc Arrastia, Maximilian Schmidt, Jennifer Landsberg, Jörg Wenzel, Dirk Schadendorf, Eva Hadaschik, Peter Maass, Jörg Schaller, Klaus Georg Griewank
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb12d92d6996f33d3ae037b0f2993eda
https://www.ncbi.nlm.nih.gov/pubmed/37257277
https://www.ncbi.nlm.nih.gov/pubmed/37257277
Autor:
Charlotte Janßen, Tobias Boskamp, Jean Le’Clerc Arrastia, Daniel Otero Baguer, Lena Hauberg-Lotte, Mark Kriegsmann, Katharina Kriegsmann, Georg Steinbuß, Rita Casadonte, Jörg Kriegsmann, Peter Maaß
Publikováno v:
Cancers; Volume 14; Issue 24; Pages: 6181
Artificial intelligence (AI) has shown potential for facilitating the detection and classification of tumors. In patients with non-small cell lung cancer, distinguishing between the most common subtypes, adenocarcinoma (ADC) and squamous cell carcino
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11b013c97b5d73516bcb5a03c330fe4b
https://pubmed.ncbi.nlm.nih.gov/36551667
https://pubmed.ncbi.nlm.nih.gov/36551667
Autor:
Philipp Jansen, Daniel Otero Baguer, Nicole Duschner, Jean Le’Clerc Arrastia, Maximilian Schmidt, Bettina Wiepjes, Dirk Schadendorf, Eva Hadaschik, Peter Maass, Jörg Schaller, Klaus Georg Griewank
Publikováno v:
Cancers; Volume 14; Issue 14; Pages: 3518
Background: Some of the most common cutaneous neoplasms are Bowen’s disease and seborrheic keratosis, a malignant and a benign proliferation, respectively. These entities represent a significant fraction of a dermatopathologists’ workload, and in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0427a5724f31c8f03fb87f960e90d039
Publikováno v:
Journal of Imaging; Volume 8; Issue 7; Pages: 202
In recent years, numerous deep-learning approaches have been developed for the analysis of histopathology Whole Slide Images (WSI). A recurrent issue is the lack of generalization ability of a model that has been trained with images of one laboratory
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5301791b30db785899f3b7cc3255cdf
https://doi.org/10.3390/jimaging8070202
https://doi.org/10.3390/jimaging8070202
Publikováno v:
Modeling, Simulation and Optimization of Complex Processes HPSC 2018 ISBN: 9783030552398
The development of new structural materials with desirable properties has become one of the most challenging tasks for engineers. High performance alloys are required for the continued development of cars, aircraft and more complex structures. The va
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::47e26f53e09ca52b8fbc390034b22f09
https://doi.org/10.1007/978-3-030-55240-4_8
https://doi.org/10.1007/978-3-030-55240-4_8
In this paper we describe an investigation into the application of deep learning methods for low-dose and sparse angle computed tomography using small training datasets. To motivate our work we review some of the existing approaches and obtain quanti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b9cc5035f8b044920173a3d3e2554fca
http://arxiv.org/abs/2003.04989
http://arxiv.org/abs/2003.04989
Publikováno v:
Machine Learning for Medical Image Reconstruction ISBN: 9783030615970
MLMIR@MICCAI
MLMIR@MICCAI
Magnetic particle imaging (MPI) is a tracer-based imaging modality with an increasing number of potential medical applications exploiting the nonlinear magnetization behavior of magnetic nanoparticles. The image reconstruction is obtained by solving
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4168002fae7a390805e386b8cce500a6
https://doi.org/10.1007/978-3-030-61598-7_11
https://doi.org/10.1007/978-3-030-61598-7_11