Virtual histology imaging of lymph nodes via dynamic full-field optical coherence tomography and deep learning to differentiate metastasisOA
Virtual histology imaging of lymph nodes via dynamic full-field optical coherence tomography and deep learning to differentiate metastasis
Shuwei Zhang;Wenhui Ren;Shu Wang;Houpu Yang;Yiyin Zhang;Xiaoxian Li;Jin Zhao;Yuanyuan Zhang;Ping Xue;Hua Kang;Hongchuan Jiang
Breast Center,Peking University People's Hospital,Beijing 100044,ChinaDepartment of Clinical Epidemiology and Biostatistics,Peking University People's Hospital,Beijing 100044,ChinaBreast Center,Peking University People's Hospital,Beijing 100044,ChinaBreast Center,Peking University People's Hospital,Beijing 100044,ChinaBreast Center,Peking University People's Hospital,Beijing 100044,ChinaDepartment of Pathology and Laboratory Medicine,Emory University,Atlanta,GA 30322,USABreast Center,Peking University People's Hospital,Beijing 100044,ChinaDepartment of Pathology,Peking University People's Hospital,Beijing 100044,ChinaDepartment of Physics and State Key Laboratory of Low-dimensional Quantum Physics,Tsinghua University,Beijing 100084,ChinaDepartment of General Surgery,Xuanwu Hospital,Capital Medical University,Beijing 100053,ChinaDepartment of Breast Surgery,Beijing Chaoyang Hospital,Capital Medical University,Beijing 100020,China
Breast cancerdynamic full field optical coherence tomographylymph nodesAI modelmetastatic status
Breast cancerdynamic full field optical coherence tomographylymph nodesAI modelmetastatic status
《癌症生物学与医学（英文版）》 2026 (3)
418-429,12
This work was supported by grants from the National Key Research and Development Program of China(Grant No.2024YFC3405303),Beijing Natural Science Foundation(Grant No.7242281 and 7244427),and Research and Development Fund of Peking University People's Hospital(Grant No.RDZH2024-03 and RDEB2025-25).
评论