deep neural network (dnn) model (Takeda)
90
Structured Review
Takeda
deep neural network (dnn) model
Deep Neural Network (Dnn) Model, supplied by Takeda, 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/deep+neural+network+(dnn)+model/neural+networks/pm39662315-34-10-4
Average 90 stars, based on 1 article reviews
Deep Neural Network (Dnn) Model, supplied by Takeda, 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/deep+neural+network+(dnn)+model/neural+networks/pm39662315-34-10-4
Average 90 stars, based on 1 article reviews
deep neural network (dnn) model - by Bioz Stars,
2026-09
90/100 stars
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Modification:Article Title: An interpretable deep learning model for hallux valgus prediction. Article Snippet: Background: This work developed an interpretable deep learning model to automatically annotate landmarks and calculate the hallux valgus angle (HVA) and the intermetatarsal angle (IMA), reducing the time and error of manual calculations by medical experts and improving the efficiency and accuracy of hallux valgus (HV) diagnosis.. Methods: A total of 2,000 foot X-ray images were manually labeled with 12 landmarks by two surgical specialists as training data for the deep learning model.. The important parts of the foot X-ray images centered on the proximal phalanx of the bunion (PH1), the first metatarsal (MT1), and the second metatarsal (MT2) were segmented using the proposed AG-UNet in the study. Labeling:Article Title: An interpretable deep learning model for hallux valgus prediction. Article Snippet: Background: This work developed an interpretable deep learning model to automatically annotate landmarks and calculate the hallux valgus angle (HVA) and the intermetatarsal angle (IMA), reducing the time and error of manual calculations by medical experts and improving the efficiency and accuracy of hallux valgus (HV) diagnosis.. Methods: A total of 2,000 foot X-ray images were manually labeled with 12 landmarks by two surgical specialists as training data for the deep learning model.. The important parts of the foot X-ray images centered on the proximal phalanx of the bunion (PH1), the first metatarsal (MT1), and the second metatarsal (MT2) were segmented using the proposed AG-UNet in the study. |