KDALDL: Knowledge Distillation-Based Adaptive Label Distribution Learning Network for Bone Age Assessment
Year: 2024
Type: article
Abstract: Deep learning-based bone age assessment (BAA) approaches have certain drawbacks, such as ignoring the correlation of age labels and simply assuming that bone development is linearly related to bone age, which can affect the accuracy of predictions. To solve these problems, a knowledge distillation-based adaptive label distribution learning method called KDALDL is proposed. The KDALDL framework comprises a teacher model and a student model, both consisting of modules for multi-scale feature extraction, feature refinement, and label distribution learning. First, a multi-scale feature extraction module is designed based on the swin transformer to extract feature information at various scales. Subsequently, these features are fed into the feature refinement module to capture the optimal image features. Then, the discrete labels obtained from the age labels through the Gaussian formula are used to train the teacher model. Finally, the teacher model’s outputs are used to train the student model through the knowledge distillation technique, which enables the student model to achieve improved results by learning from the teacher model. The proposed method is validated using the Radiological Society of North America (RSNA) dataset, which exhibits outstanding results.
Source: IEEE Access
Authors: Hao-Dong Zheng, Lei Yu, Yu-Ting Lu, Wei-Hao Zhang, Yan-Jun Yu
Institutions: Chongqing Normal University, Chongqing University
Language: English
Subfield: Archeology (arts and humanities)
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,995
OpenAlex ID: W4391248739