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SCI 3 October 2024 A pathology foundation model for cancer diagnosis and prognosis prediction (Nature, IF: 50.5) Xiyue Wang, Junhan Zhao, Eliana Marostica, Wei Yuan, Jietian Jin, Jiayu Zhang, Ruijiang Li, Hongping Tang, Kanran Wang, Yu Li, Fang Wang, Yulong Peng, Junyou Zhu, Jing Zhang, Christopher R. Jackson, Jun Zhang, Deborah Dillon, Nancy U. Lin1, Lynette Sholl1, Thomas Denize, David Meredith, Keith L. Ligon1, Sabina Signoretti, Shuji Ogino, Jeffrey A. Golden, MacLean P. Nasrallah, Xiao Han, Sen Yang & Kun-Hsing Yu CORRESPONDENCE TO: sen.yang.scu@gmail.com; Kun-Hsing Yu@hms.harvard.edu Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task. Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or sample
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