Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Jan Sellner"'
Autor:
Leonardo Ayala, Diana Mindroc-Filimon, Maike Rees, Marco Hübner, Jan Sellner, Silvia Seidlitz, Minu Tizabi, Sebastian Wirkert, Alexander Seitel, Lena Maier-Hein
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-9 (2024)
Abstract Spectral imaging has the potential to become a key technique in interventional medicine as it unveils much richer optical information compared to conventional RBG (red, green, and blue)-based imaging. Thus allowing for high-resolution functi
Externí odkaz:
https://doaj.org/article/3f1829251e2c4943801082e6ef6b7409
Autor:
Alexander Studier-Fischer, Silvia Seidlitz, Jan Sellner, Marc Bressan, Berkin Özdemir, Leonardo Ayala, Jan Odenthal, Samuel Knoedler, Karl-Friedrich Kowalewski, Caelan Max Haney, Gabriel Salg, Maximilian Dietrich, Hannes Kenngott, Ines Gockel, Thilo Hackert, Beat Peter Müller-Stich, Lena Maier-Hein, Felix Nickel
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-14 (2023)
Abstract Hyperspectral Imaging (HSI) is a relatively new medical imaging modality that exploits an area of diagnostic potential formerly untouched. Although exploratory translational and clinical studies exist, no surgical HSI datasets are openly acc
Externí odkaz:
https://doaj.org/article/07eef82b27dc45cd9e0eca8dcfbd0d70
Autor:
Alexander Studier-Fischer, Silvia Seidlitz, Jan Sellner, Berkin Özdemir, Manuel Wiesenfarth, Leonardo Ayala, Jan Odenthal, Samuel Knödler, Karl Friedrich Kowalewski, Caelan Max Haney, Isabella Camplisson, Maximilian Dietrich, Karsten Schmidt, Gabriel Alexander Salg, Hannes Götz Kenngott, Tim Julian Adler, Nicholas Schreck, Annette Kopp-Schneider, Klaus Maier-Hein, Lena Maier-Hein, Beat Peter Müller-Stich, Felix Nickel
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-14 (2022)
Abstract Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral informati
Externí odkaz:
https://doaj.org/article/ebb9fc0545f64ebba5e2b0ae635ad0f5
Autor:
Matthias A. Fink, Klaus Kades, Arved Bischoff, Martin Moll, Merle Schnell, Maike Küchler, Gregor Köhler, Jan Sellner, Claus Peter Heussel, Hans-Ulrich Kauczor, Heinz-Peter Schlemmer, Klaus Maier-Hein, Tim F. Weber, Jens Kleesiek
Publikováno v:
Radiol Artif Intell
PURPOSE: To train a deep natural language processing (NLP) model, using data mined structured oncology reports (SOR), for rapid tumor response category (TRC) classification from free-text oncology reports (FTOR) and to compare its performance with hu
Autor:
Tom Rix, Marco Hübner, Kris K. Dreher, Jan-Hinrich Nölke, Leonardo Ayala, Melanie Schellenberg, Jan Sellner, Silvia Seidlitz, Alexander Studier-Fischer, Beat Müller-Stich, Felix Nickel, Alexander Seitel, Lena Maier-Hein
Publikováno v:
Multimodal Biomedical Imaging XVII.
Autor:
Alexander Studier-Fischer, Silvia Seidlitz, Jan Sellner, Berkin Özdemir, Manuel Wiesenfarth, Leonardo Ayala, Jan Odenthal, Samuel Knödler, Karl-Friedrich Kowalewski, Caelan Max Haney, Isabella Camplisson, Maximilian Dietrich, Karsten Schmidt, Gabriel Alexander Salg, Hannes Götz Kenngott, Tim Julian Adler, Nicholas Schreck, Annette Kopp-Schneider, Klaus Maier-Hein, Lena Maier-Hein, Beat Peter Müller-Stich, Felix Nickel
Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22a6c28fc198537e1afeb31a659a11f8
https://doi.org/10.21203/rs.3.rs-1328439/v1
https://doi.org/10.21203/rs.3.rs-1328439/v1
Autor:
Silvia Seidlitz, Jan Sellner, Jan Odenthal, Berkin Özdemir, Alexander Studier-Fischer, Samuel Knödler, Leonardo Ayala, Tim J. Adler, Hannes G. Kenngott, Minu Tizabi, Martin Wagner, Felix Nickel, Beat P. Müller-Stich, Lena Maier-Hein
Publikováno v:
Medical Image Analysis
Semantic image segmentation is an important prerequisite for context-awareness and autonomous robotics in surgery. The state of the art has focused on conventional RGB video data acquired during minimally invasive surgery, but full-scene semantic seg
Autor:
Leonardo Ayala, Tim J. Adler, Silvia Seidlitz, Sebastian Wirkert, Christina Engels, Alexander Seitel, Jan Sellner, Alexey Aksenov, Matthias Bodenbach, Pia Bader, Sebastian Baron, Anant Vemuri, Manuel Wiesenfarth, Nicholas Schreck, Diana Mindroc, Minu Tizabi, Sebastian Pirmann, Brittaney Everitt, Annette Kopp-Schneider, Dogu Teber, Lena Maier-Hein
Publikováno v:
medRxiv
Laparoscopic surgery has evolved as a key technique for cancer diagnosis and therapy. While characterization of the tissue perfusion is crucial in various procedures, such as partial nephrectomy, doing so by means of visual inspection remains highly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71da4e5a62db79b8ec7f183507f227ef
Autor:
Klaus Kades, Jan Sellner, Gregor Koehler, Peter M Full, T Y Emmy Lai, Jens Kleesiek, Klaus H Maier-Hein
BACKGROUND Natural Language Understanding enables automatic extraction of relevant information from clinical text data, which are acquired every day in hospitals. In 2018, the language model Bidirectional Encoder Representations from Transformers (BE
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::097cee12d62bb8aa4b9e96891e737773
https://doi.org/10.2196/preprints.22795
https://doi.org/10.2196/preprints.22795
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030209834
MPRSS
MPRSS
Pain and emotions reveal important information about the state of a person and are often expressed via the face. Most of the time, systems which analyse these states consider only one type of expression. For pain, the medical context is a common scen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::071ea1b44f104166b0374c064837185f
https://doi.org/10.1007/978-3-030-20984-1_9
https://doi.org/10.1007/978-3-030-20984-1_9