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  • Title: Incorporated region detection and classification using deep convolutional networks for bone age assessment.
    Author: Bui TD, Lee JJ, Shin J.
    Journal: Artif Intell Med; 2019 Jun; 97():1-8. PubMed ID: 31202395.
    Abstract:
    Bone age assessment plays an important role in the endocrinology and genetic investigation of patients. In this paper, we proposed a deep learning-based approach for bone age assessment by integration of the Tanner-Whitehouse (TW3) methods and deep convolution networks based on extracted regions of interest (ROI)-detection and classification using Faster-RCNN and Inception-v4 networks, respectively. The proposed method allows exploration of expert knowledge from TW3 and features engineering from deep convolution networks to enhance the accuracy of bone age assessment. The experimental results showed a mean absolute error of about 0.59 years between expert radiologists and the proposed method, which is the best performance among state-of-the-art methods.
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