Ridge regression neural network for pediatric bone age assessment

Salim, Ibrahim; Hamza, A. Ben

doi:10.1007/s11042-021-10935-8

Cited by 29 publications

(15 citation statements)

References 27 publications

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“…For example, in [14] the authors achieved a mean MAE equal to 6.05 months. In another paper [15] authors obtained mean MAE equal to 6.38. However, it is important to underline that the images in the RSNA 2017 dataset had a minimal resolution 512 × 512 pixels and represented a slightly different domain (only the hand).…”

Section: Discussionmentioning

confidence: 91%

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Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

et al. 2022

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Age prediction from X-rays is an interesting research topic important for clinical applications such as biological maturity assessment. It is also useful in many other practical applications, including sports or forensic investigations for age verification purposes. Research on these issues is usually carried out using high-resolution X-ray scans of parts of the body, such as images of the hands or images of the chest. In this study, we used low-resolution, dual-energy, full-body X-ray absorptiometry images to train deep learning models to predict age. In particular, we proposed a preprocessing framework and adapted many partially pretrained convolutional neural network (CNN) models to predict the age of children and young adults. We used a new dataset of 910 multispectral images that were weakly annotated by specialists. The experimental results showed that the proposed preprocessing techniques and the adapted approach to the CNN model achieved a discrepancy between chronological age and predicted age of around 15.56 months for low-resolution whole-body X-rays. Furthermore, we found that the main factor that influenced age prediction scores was spatial features, not multispectral features.

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Section: Discussionmentioning

confidence: 91%

“…They achieved an average MAE of 6.05 months [14]. Salim and Hamza introduced a two-stage approach for bone age assessment using segmentation and ridge regression [15]. The MAE of the proposed solution was equal to 6.38 months.…”

Section: Introductionmentioning

confidence: 99%

Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

et al. 2022

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“…RSNA-12,611 Image FR-CNN, RNN, AF-SFO Good performance by the proposed Otsu segmentation model and BAA model compared to other machine learning algorithmsThe proposed model's complexity for implementation and medical use[9] RSNA-12,611 Image Mask R-CNN and VGG-19The proposed model also performed well on the chest X-ray dataset.Using hand annotation for detecting hand image bounds. [5] Public-DHA -1380 Image VGG-19, GoogleNet AlexNet Segmented images and tested multiple deep learning methods on the data to achieve better results Lower accuracy than the proposed model with the same dataset [15] DR-1666 Image AlexNet Improved age detection in the [-1, 0, 1] age error interval by up to 96%.…”

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confidence: 91%

“…Ongoing progress in computer technology accelerates the development of techniques resolving these problems, thus; numerous automated BAA approaches have been proposed based on image processing and computer vision techniques [9,10,11]. They often deal with BAA as a classification or regression problem containing fundamental stages as hand segmentation, ROI detection, feature extraction and classifier or regressor model [1].…”

Section: Introductionmentioning

confidence: 99%

A Bone Age Assessment based on a hybrid Knowledge Distillation Paradigm using single ROI

Mardanpour

Sepahvand

Abdali-Mohammadi

et al. 2022

Preprint

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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.

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“…The MAE is 6.2 months. Salim and Hamza [24] proposed a RidgeNet model, which uses a Mask-RCNN for hand segmentation, Ridge regression to complete bone age estimation, and VGG19 as the backbone.…”

Section: Introductionmentioning

confidence: 99%

Automatic Bone Age Assessment of Adolescents Based on Weakly-Supervised Deep Convolutional Neural Networks

Zhang

Sun

et al. 2021

IEEE Access

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Hand bone age, as the biological age of humans, can accurately reflect the development level and maturity of individuals. Bone age assessment results of adolescents can provide a theoretical basis for their growth and development and height prediction. In this study, a deep convolutional neural network (CNN) model based on fine-grained image classification is proposed, using a hand bone image dataset provided by the Radiological Society of North America (RSNA) as the research object. This model can automatically locate informative regions and extract local features in the process of hand bone image recognition, and then, the extracted local features are combined with global features of a complete image for bone age classification. This method can achieve end-to-end bone age assessment without any image annotation information (except bone age tags), improving the speed and accuracy of bone age assessment. Experimental results show that the proposed method achieves 66.38% and 68.63% recognition accuracy of males and females on the RSNA dataset, and the mean absolute errors are 3.71 ± 7.55 and 3.81 ± 7.74 months for males and females, respectively. The test time for each image is approximately 35 ms. This method achieves good performance and outperforms existing methods in bone age assessment based on weakly supervised fine-grained image classification.INDEX TERMS Bone age assessment, Deep learning, Convolutional neural network, Fine-grained image.

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Ridge regression neural network for pediatric bone age assessment

Cited by 29 publications

References 27 publications

Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

A Bone Age Assessment based on a hybrid Knowledge Distillation Paradigm using single ROI

Automatic Bone Age Assessment of Adolescents Based on Weakly-Supervised Deep Convolutional Neural Networks

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