Zobrazeno 1 - 10
of 2 265
pro vyhledávání: '"A Kather"'
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:
Lammert, Jacqueline, Pfarr, Nicole, Kuligin, Leonid, Mathes, Sonja, Dreyer, Tobias, Modersohn, Luise, Metzger, Patrick, Ferber, Dyke, Kather, Jakob Nikolas, Truhn, Daniel, Adams, Lisa Christine, Bressem, Keno Kyrill, Lange, Sebastian, Schwamborn, Kristina, Boeker, Martin, Kiechle, Marion, Schatz, Ulrich A., Bronger, Holger, Tschochohei, Maximilian
Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poor prognosis. Molecular tumor boards accelerate access to effective the
Externí odkaz:
http://arxiv.org/abs/2409.00544
Autor:
Neidlinger, Peter, Nahhas, Omar S. M. El, Muti, Hannah Sophie, Lenz, Tim, Hoffmeister, Michael, Brenner, Hermann, van Treeck, Marko, Langer, Rupert, Dislich, Bastian, Behrens, Hans Michael, Röcken, Christoph, Foersch, Sebastian, Truhn, Daniel, Marra, Antonio, Saldanha, Oliver Lester, Kather, Jakob Nikolas
Advancements in artificial intelligence have driven the development of numerous pathology foundation models capable of extracting clinically relevant information. However, there is currently limited literature independently evaluating these foundatio
Externí odkaz:
http://arxiv.org/abs/2408.15823
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:
Arasteh, Soroosh Tayebi, Lotfinia, Mahshad, Bressem, Keno, Siepmann, Robert, Ferber, Dyke, Kuhl, Christiane, Kather, Jakob Nikolas, Nebelung, Sven, Truhn, Daniel
Large language models (LLMs) have advanced the field of artificial intelligence (AI) in medicine. However LLMs often generate outdated or inaccurate information based on static training datasets. Retrieval augmented generation (RAG) mitigates this by
Externí odkaz:
http://arxiv.org/abs/2407.15621
Autor:
Ferber, Dyke, Hilgers, Lars, Wiest, Isabella C., Leßmann, Marie-Elisabeth, Clusmann, Jan, Neidlinger, Peter, Zhu, Jiefu, Wölflein, Georg, Lammert, Jacqueline, Tschochohei, Maximilian, Böhme, Heiko, Jäger, Dirk, Aldea, Mihaela, Truhn, Daniel, Höper, Christiane, Kather, Jakob Nikolas
Matching cancer patients to clinical trials is essential for advancing treatment and patient care. However, the inconsistent format of medical free text documents and complex trial eligibility criteria make this process extremely challenging and time
Externí odkaz:
http://arxiv.org/abs/2407.13463
Denoising diffusion models offer a promising approach to accelerating magnetic resonance imaging (MRI) and producing diagnostic-level images in an unsupervised manner. However, our study demonstrates that even tiny worst-case potential perturbations
Externí odkaz:
http://arxiv.org/abs/2406.16983
Autor:
Khader, Firas, Nahhas, Omar S. M. El, Han, Tianyu, Müller-Franzes, Gustav, Nebelung, Sven, Kather, Jakob Nikolas, Truhn, Daniel
The Transformer model has been pivotal in advancing fields such as natural language processing, speech recognition, and computer vision. However, a critical limitation of this model is its quadratic computational and memory complexity relative to the
Externí odkaz:
http://arxiv.org/abs/2406.01314
Autor:
Ferber, Dyke, Nahhas, Omar S. M. El, Wölflein, Georg, Wiest, Isabella C., Clusmann, Jan, Leßman, Marie-Elisabeth, Foersch, Sebastian, Lammert, Jacqueline, Tschochohei, Maximilian, Jäger, Dirk, Salto-Tellez, Manuel, Schultz, Nikolaus, Truhn, Daniel, Kather, Jakob Nikolas
Multimodal artificial intelligence (AI) systems have the potential to enhance clinical decision-making by interpreting various types of medical data. However, the effectiveness of these models across all medical fields is uncertain. Each discipline p
Externí odkaz:
http://arxiv.org/abs/2404.04667