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[Standardizing breast cancer digital pathology databases for artificial intelligence: practice and reflection].

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Zhonghua bing li xue za zhi = Chinese journal of pathology 📖 저널 OA 0% 2022: 0/1 OA 2023: 0/1 OA 2024: 0/1 OA 2025: 0/12 OA 2026: 0/18 OA 2022~2026 2026 Vol.55(3) p. 221-228
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Li FL, Bu H, Zhang Z

📝 환자 설명용 한 줄

The traditional paradigm of pathology diagnosis faces challenges like data fragmentation and inefficiency in the era of big data and artificial intelligence, necessitating a digital transformation bui

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APA Li FL, Bu H, Zhang Z (2026). [Standardizing breast cancer digital pathology databases for artificial intelligence: practice and reflection].. Zhonghua bing li xue za zhi = Chinese journal of pathology, 55(3), 221-228. https://doi.org/10.3760/cma.j.cn112151-20251207-00806
MLA Li FL, et al.. "[Standardizing breast cancer digital pathology databases for artificial intelligence: practice and reflection].." Zhonghua bing li xue za zhi = Chinese journal of pathology, vol. 55, no. 3, 2026, pp. 221-228.
PMID 41795984 ↗

Abstract

The traditional paradigm of pathology diagnosis faces challenges like data fragmentation and inefficiency in the era of big data and artificial intelligence, necessitating a digital transformation built upon high-quality, standardized databases. This article reviews global efforts in digital pathology database construction and details our team's pilot practice in developing a specialized breast pathology database. We share our approach to case enrollment, systematic data collection, and standardized slide digitization. Key issues, including data schema design, multi-center integration, scanning protocols, and collaborative models, are discussed to inspire industry-wide standardization and support the sustainable development of digital pathology and artificial intelligence in China.

🏷️ 키워드 / MeSH 📖 같은 키워드 OA만

🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반