Attention-based multiple-instance learning for Pediatric bone age assessment with efficient and interpretable. (January 2023)

• Record Type: Journal Article
• Authors: Wang, Chong; Wu, Yang; Wang, Chen; Zhou, Xuezhi; Niu, Yanxiang; Zhu, Yu; Gao, Xudong; Wang, Chang; Yu, Yi
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 1
• Publication Date: 2023-01
• Subjects: Bone age assessment -- Multiple-instance learning -- Deep learning -- Hand radiograph; BAA bone age assessment -- ROI region of interest -- GP Greulich-Pyle -- TW Tanner-Whitehouse -- CNN convolutional neural network -- MIL multiple-instance learning -- RSNA Radiological Society of North America -- MAE Mean Absolute Error -- ViT-Base Vision Transformer Base; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104028
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Multi-instance learning-based attention networks for bone age assessment. Data efficiency is achieved through feature extraction networks. The proposed model is highly interpretable. Abstract: Pediatric bone age assessment (BAA) is a common clinical technique for evaluating children's endocrine, genetic, and growth disorders. However, the deep learning BAA method based on global images neglects fine-grained concerns, and regions of interest (ROIs) need additional annotation and complex processing. To overcome these shortcomings, we proposed an interpretable deep-learning architecture based on multiple-instance learning to address BAA efficiently without additional annotations. We cropped the entire image into small patches and got patch features by feature extraction network. Then, an attention backbone ranked feature vectors of the entire image and aggregates its information according to its relative importance. Finally, each image's features and gender were aggregated to predict bone age. The proposed method can identify ROIs by attention-based multi-instance aggregation without additional labels and produce interpretable heatmaps. Moreover, by cropping the complete image into patches and reducing the dimensionality, the proposed model can notice the fine-grained information of the image and improve the model training speed. We validated the proposed method in the Radiological Society of North America 2017 dataset. The results showed that the proposed model achieved an advanced performance of MAE 4.17 months. Furthermore, the visualization results indicated that the proposed model was highly interpretable, which can localize the ROIs without spatial labeling. In conclusion, a novel method for high performance and interpretable bone age prediction without additional manual annotations has been developed, which can be used to effectively assess the pediatric's bone age.