Výsledky vyhledávání - "Shah, Rajal B"

Report

Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer

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