Zobrazeno 1 - 10
of 2 456
pro vyhledávání: '"TITUS, J."'
Autor:
Mittmann, Gesa, Laiouar-Pedari, Sara, Mehrtens, Hendrik A., Haggenmüller, Sarah, Bucher, Tabea-Clara, Chanda, Tirtha, Gaisa, Nadine T., Wagner, Mathias, Klamminger, Gilbert Georg, Rau, Tilman T., Neppl, Christina, Compérat, Eva Maria, Gocht, Andreas, Hämmerle, Monika, Rupp, Niels J., Westhoff, Jula, Krücken, Irene, Seidl, Maximillian, Schürch, Christian M., Bauer, Marcus, Solass, Wiebke, Tam, Yu Chun, Weber, Florian, Grobholz, Rainer, Augustyniak, Jaroslaw, Kalinski, Thomas, Hörner, Christian, Mertz, Kirsten D., Döring, Constanze, Erbersdobler, Andreas, Deubler, Gabriele, Bremmer, Felix, Sommer, Ulrich, Brodhun, Michael, Griffin, Jon, Lenon, Maria Sarah L., Trpkov, Kiril, Cheng, Liang, Chen, Fei, Levi, Angelique, Cai, Guoping, Nguyen, Tri Q., Amin, Ali, Cimadamore, Alessia, Shabaik, Ahmed, Manucha, Varsha, Ahmad, Nazeel, Messias, Nidia, Sanguedolce, Francesca, Taheri, Diana, Baraban, Ezra, Jia, Liwei, Shah, Rajal B., Siadat, Farshid, Swarbrick, Nicole, Park, Kyung, Hassan, Oudai, Sakhaie, Siamak, Downes, Michelle R., Miyamoto, Hiroshi, Williamson, Sean R., Holland-Letz, Tim, Schneider, Carolin V., Kather, Jakob Nikolas, Tolkach, Yuri, Brinker, Titus J.
The aggressiveness of prostate cancer, the most common cancer in men worldwide, is primarily assessed based on histopathological data using the Gleason scoring system. While artificial intelligence (AI) has shown promise in accurately predicting Glea
Externí odkaz:
http://arxiv.org/abs/2410.15012
Autor:
Chanda, Tirtha, Haggenmueller, Sarah, Bucher, Tabea-Clara, Holland-Letz, Tim, Kittler, Harald, Tschandl, Philipp, Heppt, Markus V., Berking, Carola, Utikal, Jochen S., Schilling, Bastian, Buerger, Claudia, Navarrete-Dechent, Cristian, Goebeler, Matthias, Kather, Jakob Nikolas, Schneider, Carolin V., Durani, Benjamin, Durani, Hendrike, Jansen, Martin, Wacker, Juliane, Wacker, Joerg, Consortium, Reader Study, Brinker, Titus J.
Artificial intelligence (AI) systems have substantially improved dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing clinicians' confidence and trust in AI-driven decisions. Despite these advancements
Externí odkaz:
http://arxiv.org/abs/2409.13476
Autor:
Clusmann, Jan, Ferber, Dyke, Wiest, Isabella C., Schneider, Carolin V., Brinker, Titus J., Foersch, Sebastian, Truhn, Daniel, Kather, Jakob N.
Vision-language artificial intelligence models (VLMs) possess medical knowledge and can be employed in healthcare in numerous ways, including as image interpreters, virtual scribes, and general decision support systems. However, here, we demonstrate
Externí odkaz:
http://arxiv.org/abs/2407.18981
Autor:
Hetz, Martin J., Carl, Nicolas, Haggenmüller, Sarah, Wies, Christoph, Michel, Maurice Stephan, Wessels, Frederik, Brinker, Titus J.
Large Language Models (LLMs) are revolutionizing medical Question-Answering (medQA) through extensive use of medical literature. However, their performance is often hampered by outdated training data and a lack of explainability, which limits clinica
Externí odkaz:
http://arxiv.org/abs/2406.01428
Clinical dermatology necessitates precision and innovation for efficient diagnosis and treatment of various skin conditions. This paper introduces the development of a cutting-edge hyperspectral dermatoscope (the Hyperscope) tailored for human skin a
Externí odkaz:
http://arxiv.org/abs/2403.00612
Autor:
Heinlein, Lukas, Maron, Roman C., Hekler, Achim, Haggenmüller, Sarah, Wies, Christoph, Utikal, Jochen S., Meier, Friedegund, Hobelsberger, Sarah, Gellrich, Frank F., Sergon, Mildred, Hauschild, Axel, French, Lars E., Heinzerling, Lucie, Schlager, Justin G., Ghoreschi, Kamran, Schlaak, Max, Hilke, Franz J., Poch, Gabriela, Korsing, Sören, Berking, Carola, Heppt, Markus V., Erdmann, Michael, Haferkamp, Sebastian, Drexler, Konstantin, Schadendorf, Dirk, Sondermann, Wiebke, Goebeler, Matthias, Schilling, Bastian, Krieghoff-Henning, Eva, Brinker, Titus J.
Early detection of melanoma, a potentially lethal type of skin cancer with high prevalence worldwide, improves patient prognosis. In retrospective studies, artificial intelligence (AI) has proven to be helpful for enhancing melanoma detection. Howeve
Externí odkaz:
http://arxiv.org/abs/2401.14193
Deep Neural Networks have shown promising classification performance when predicting certain biomarkers from Whole Slide Images in digital pathology. However, the calibration of the networks' output probabilities is often not evaluated. Communicating
Externí odkaz:
http://arxiv.org/abs/2312.09719
Autor:
Chamarthi, Sireesha, Fogelberg, Katharina, Maron, Roman C., Brinker, Titus J., Niebling, Julia
The potential of deep neural networks in skin lesion classification has already been demonstrated to be on-par if not superior to the dermatologists diagnosis. However, the performance of these models usually deteriorates when the test data differs s
Externí odkaz:
http://arxiv.org/abs/2310.03432
Autor:
Nicolas Carl, Franziska Schramm, Sarah Haggenmüller, Jakob Nikolas Kather, Martin J. Hetz, Christoph Wies, Maurice Stephan Michel, Frederik Wessels, Titus J. Brinker
Publikováno v:
npj Precision Oncology, Vol 8, Iss 1, Pp 1-17 (2024)
Abstract Large language models (LLMs) are undergoing intensive research for various healthcare domains. This systematic review and meta-analysis assesses current applications, methodologies, and the performance of LLMs in clinical oncology. A mixed-m
Externí odkaz:
https://doaj.org/article/169dc2e4a5c747dcb6d8a8cf244c31cc
Autor:
Wies, Christoph, Schneider, Lucas, Haggenmueller, Sarah, Bucher, Tabea-Clara, Hobelsberger, Sarah, Heppt, Markus V., Ferrara, Gerardo, Krieghoff-Henning, Eva I., Brinker, Titus J.
Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based supp
Externí odkaz:
http://arxiv.org/abs/2309.03494