RAGCN: Region aggregation graph convolutional network for bone age assessment from X-ray images

X Li, Y Jiang, Y Liu, J Zhang, S Yin… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Rapid and accurate measurement of bone age from hand X-ray images is a significant task
for children's maturity assessment and metabolic disorders diagnosis. With the development …

Attention-guided discriminative region localization and label distribution learning for bone age assessment

C Chen, Z Chen, X Jin, L Li, W Speier… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Bone age assessment (BAA) is clinically important as it can be used to diagnose endocrine
and metabolic disorders during child development. Existing deep learning based methods …

Multi-task deep supervision on attention R2U-net for brain tumor segmentation

S Ma, J Tang, F Guo - Frontiers in Oncology, 2021 - frontiersin.org
Accurate automatic medical image segmentation technology plays an important role for the
diagnosis and treatment of brain tumor. However, simple deep learning models are difficult …

Self-supervised attention mechanism for pediatric bone age assessment with efficient weak annotation

C Liu, H Xie, Y Zhang - IEEE Transactions on Medical Imaging, 2020 - ieeexplore.ieee.org
Pediatric bone age assessment (BAA) is a common clinical practice to investigate
endocrinology, genetic and growth disorders of children. Different specific bone parts are …

[PDF][PDF] Bone age assessment from articular surface and epiphysis using deep neural networks

Y Deng, Y Chen, Q He, X Wang, Y Liao… - Math. Biosci …, 2023 - pdfs.semanticscholar.org
Bone age assessment is of great significance to genetic diagnosis and endocrine diseases.
Traditional bone age diagnosis mainly relies on experienced radiologists to examine the …

Attention-based multiple-instance learning for Pediatric bone age assessment with efficient and interpretable

C Wang, Y Wu, C Wang, X Zhou, Y Niu, Y Zhu… - … Signal Processing and …, 2023 - Elsevier
Pediatric bone age assessment (BAA) is a common clinical technique for evaluating
children's endocrine, genetic, and growth disorders. However, the deep learning BAA …

[HTML][HTML] SMANet: multi-region ensemble of convolutional neural network model for skeletal maturity assessment

Y Zhang, W Zhu, K Li, D Yan, H Liu, J Bai… - … Imaging in Medicine …, 2022 - ncbi.nlm.nih.gov
Background Bone age assessment (BAA) is a crucial research topic in pediatric radiology.
Interest in the development of automated methods for BAA is increasing. The current BAA …

Multi-objective segmentation approach for bone age assessment using parameter tuning-based U-net architecture

S Deshmukh, A Khaparde - Multimedia Tools and Applications, 2022 - Springer
Bone age assessment investigates the ossification improvement for estimating the skeletal
age of the pediatrics for analyzing their skeletal growth and forecast their future adult height …

Multi-scale multi-reception attention network for bone age assessment in X-ray images

Z Yang, C Cong, M Pagnucco, Y Song - Neural Networks, 2023 - Elsevier
Bone age assessment plays a significant role in estimating bone maturity. However,
radiograph/X-ray images of hand bones contain a large amount of redundant information …

Adaptive Critical Region Extraction Net via relationship modeling for bone age assessment

M Chen, J Wu, F Luo, J Zhang, M Zhang… - … Signal Processing and …, 2023 - Elsevier
Bone age assessment is an efficient clinical practice to diagnose children's endocrine,
genetic, and growth disorders. Guided by clinical diagnostic methods, local critical regions …