semi-supervised cnn model (Anhui Medical University)
90
Structured Review
Anhui Medical University
semi-supervised cnn model
Semi Supervised Cnn Model, supplied by Anhui Medical University, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/semi-supervised+cnn+model/semi+supervised+cnn+model/pm40102799-45-43-16
Average 90 stars, based on 1 article reviews
Semi Supervised Cnn Model, supplied by Anhui Medical University, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/semi-supervised+cnn+model/semi+supervised+cnn+model/pm40102799-45-43-16
Average 90 stars, based on 1 article reviews
semi-supervised cnn model - by Bioz Stars,
2026-09
90/100 stars
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Diagnostic Assay:Article Title: A semi-supervised convolutional neural network for diagnosis of pancreatic ductal adenocarcinoma based on EUS-FNA cytological images. Article Snippet: A total of 210 patients with pancreatic mass were admitted to The First Affiliated Hospital of Anhui Medical University, The First Affiliated Hospital of University of Science and Technology of China, Anqing Hospital Conclusion Not merely tremendous preparatory work was drastically reduced, the semi-supervised CNN model could effectively identify PDAC cell clusters in EUS-FNA cytological smears which achieved analogically diagnostic capability compared with senior cytopathologists, and showed outstanding performance in assisting to categorize “atypical” cases where manual diagnosis is controversial. Biomarker Discovery:Article Title: A semi-supervised convolutional neural network for diagnosis of pancreatic ductal adenocarcinoma based on EUS-FNA cytological images. Article Snippet: A total of 210 patients with pancreatic mass were admitted to The First Affiliated Hospital of Anhui Medical University, The First Affiliated Hospital of University of Science and Technology of China, Anqing Hospital Conclusion Not merely tremendous preparatory work was drastically reduced, the semi-supervised CNN model could effectively identify PDAC cell clusters in EUS-FNA cytological smears which achieved analogically diagnostic capability compared with senior cytopathologists, and showed outstanding performance in assisting to categorize “atypical” cases where manual diagnosis is controversial. |