Multi-modal AI for comprehensive breast cancer prognostication

Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. Recurrence risk assessment plays a crucial role in personalizing treatment. Current methods, including genomic assays, have limited accuracy and clinical utility, leading to suboptimal decisions for ma...

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Hauptverfasser:	Witowski, Jan, Zeng, Ken, Cappadona, Joseph, Elayoubi, Jailan, Chiru, Elena Diana, Chan, Nancy, Kang, Young-Joon, Howard, Frederick, Ostrovnaya, Irina, Fernandez-Granda, Carlos, Schnabel, Freya, Ozerdem, Ugur, Liu, Kangning, Steinsnyder, Zoe, Thakore, Nitya, Sadic, Mohammad, Yeung, Frank, Liu, Elisa, Hill, Theodore, Swett, Benjamin, Rigau, Danielle, Clayburn, Andrew, Speirs, Valerie, Vetter, Marcus, Sojak, Lina, Soysal, Simone Muenst, Baumhoer, Daniel, Choucair, Khalil, Zong, Yu, Daoud, Lina, Saad, Anas, Abdulsattar, Waleed, Beydoun, Rafic, Pan, Jia-Wern, Makmur, Haslina, Teo, Soo-Hwang, Pak, Linda Ma, Angel, Victor, Zilenaite-Petrulaitiene, Dovile, Laurinavicius, Arvydas, Klar, Natalie, Piening, Brian D, Bifulco, Carlo, Jun, Sun-Young, Yi, Jae Pak, Lim, Su Hyun, Brufsky, Adam, Esteva, Francisco J, Pusztai, Lajos, LeCun, Yann, Geras, Krzysztof J
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition
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