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Bone age assessment (BAA) has been widely used in clinical area from pediatric endocrine diagnosis to estimating height of children for adulthood. Advancements in technology has accelerated the evolution of BAA methodologies, one of which is deep learning algorithms, which overcome the drawbacks of conventional approaches. In spite of excellent effectiveness of deep neural networks in detection of the correct class for bone age, they have a significant degree of complexity due to the numerous parameters they employ for each ROI. Therefore, they are not suitable for implementation in edge devices with limited resources. In this paper, we propose a BAA method using a hybrid knowledge distillation (KD) paradigm in order to conquer this difficulty by mapping different ROIs into a single ROI. In this regard, the student receives knowledge from a teacher network that has been pre-trained on six ROIs including bones of five fingers and the wrist, transfers the knowledge of its final response layer and internal layers to the student. Then, six student models each of which is constructed based on just one of these ROIs, while receiving the information of the teacher model by matching the feature maps of intermediate layers and last output of teacher with those of student in distillation modules. Empirical results on Digital Hand Atlas (DHA) report that our student model trained on one ROI obtains 95% accuracy on 19 classes of bone age makes it appropriate for medical IoT deployment. Also, it has competitive performance compared to the other state-of-the-art BAA studies, and performance analysis of our KD with three base KD models indicates superiority of our introduced method.

Bone age assessment (BAA) has been widely used in clinical area from pediatric endocrine diagnosis to estimating height of children for adulthood. Advancements in technology has accelerated the evolution of BAA methodologies, one of which is deep learning algorithms, which overcome the drawbacks of conventional approaches. In spite of excellent effectiveness of deep neural networks in detection of the correct class for bone age, they have a significant degree of complexity due to the numerous parameters they employ for each ROI. Therefore, they are not suitable for implementation in edge devices with limited resources. In this paper, we propose a BAA method using a hybrid knowledge distillation (KD) paradigm in order to conquer this difficulty by mapping different ROIs into a single ROI. In this regard, the student receives knowledge from a teacher network that has been pre-trained on six ROIs including bones of five fingers and the wrist, transfers the knowledge of its final response layer and internal layers to the student. Then, six student models each of which is constructed based on just one of these ROIs, while receiving the information of the teacher model by matching the feature maps of intermediate layers and last output of teacher with those of student in distillation modules. Empirical results on Digital Hand Atlas (DHA) report that our student model trained on one ROI obtains 95% accuracy on 19 classes of bone age makes it appropriate for medical IoT deployment. Also, it has competitive performance compared to the other state-of-the-art BAA studies, and performance analysis of our KD with three base KD models indicates superiority of our introduced method.