Automated Assessment of Bone Age Using Deep Learning and Gaussian Process Regression

Steenkiste, Tom Van; Ruyssinck, Joeri; Janssens, Olivier; Vandersmissen, Baptist; Vandecasteele, Florian; Devolder, Pieter; Achten, Eric; Hoecke, Sofie Van; Deschrijver, Dirk; Dhaene, Tom

doi:10.1109/embc.2018.8512334

Cited by 22 publications

(14 citation statements)

References 16 publications

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“…In these equations, σ is the logistic sigmoid function, x t represents the input sequence x at time t consisting of To tune the hyperparameters n 1 , n 2 , p 1 and p 2 of the network, Bayesian optimization (BO) is used which is a powerful strategy to optimize hyperparameters of medical machine learning models [41], [33], [42]. It converges the network architecture to an optimal design for accurate prediction of unseen data.…”

Section: E Long Short-term Memory Neural Networkmentioning

confidence: 99%

Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks

Steenkiste

Groenendaal

Deschrijver

et al. 2019

IEEE J. Biomed. Health Inform.

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102

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Sleep apnea is one of the most common sleep disorders and the consequences of undiagnosed sleep apnea can be very severe, ranging from increased blood pressure to heart failure. However, many people are often unaware of their condition. The gold standard for diagnosing sleep apnea is an overnight polysomnography in a dedicated sleep laboratory. Yet, these tests are expensive and beds are limited as trained staff needs to analyze the entire recording. An automated detection method would allow a faster diagnosis and more patients to be analyzed. Most algorithms for automated sleep apnea detection use a set of human-engineered features, potentially missing important sleep apnea markers. In this work, we present an algorithm based on state-of-the-art deep learning models for automatically extracting features and detecting sleep apnea events in respiratory signals. The algorithm is evaluated on the Sleep-Heart-Health-Study-1 dataset and provides per-epoch sensitivity and specificity scores comparable to the state-of-the-art. Furthermore, when these predictions are mapped to the apnea-hypopnea-index, a considerable improvement in per-patient scoring is achieved over conventional methods. This work presents a powerful aid for trained staff to quickly diagnose sleep apnea.

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Section: E Long Short-term Memory Neural Networkmentioning

confidence: 99%

Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks

Steenkiste

Groenendaal

Deschrijver

et al. 2019

IEEE J. Biomed. Health Inform.

Self Cite

102

View full text Add to dashboard Cite

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“…The authors obtained efficient performance, implement novel preprocessing, and data augmentation techniques along with deep-learning models. In several studies i.e., [ 83 , 85 ], researchers improved the accuracy of the model by adding some normalized pre-processing steps that effectively improved the generalize capabilities of the model without changing the architecture of the model. Consequently, most of the researchers focus on the use of data augmentation strategies and techniques to make the network of models, e.g., the network of the CNN model becomes more robust by using these techniques and improve the performance model.…”

Section: Discussionmentioning

confidence: 99%

“…The model rotates a single radiograph 18 times at [−90, 90] degrees. Hence the sensitivity of the model is enhanced due to rotation and flips of the input image that increases the overall prediction performance of the model instead of using a simple deep-learning model [ 83 ]. Another CaffeNet-based Convolution Neural Network model that has low complexity compared to other deep learning models is presented in [ 84 ].…”

Section: Deep-learning Models For Bone Age Assessmentmentioning

confidence: 99%

Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

et al. 2020

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Deep learning is a quite useful and proliferating technique of machine learning. Various applications, such as medical images analysis, medical images processing, text understanding, and speech recognition, have been using deep learning, and it has been providing rather promising results. Both supervised and unsupervised approaches are being used to extract and learn features as well as for the multi-level representation of pattern recognition and classification. Hence, the way of prediction, recognition, and diagnosis in various domains of healthcare including the abdomen, lung cancer, brain tumor, skeletal bone age assessment, and so on, have been transformed and improved significantly by deep learning. By considering a wide range of deep-learning applications, the main aim of this paper is to present a detailed survey on emerging research of deep-learning models for bone age assessment (e.g., segmentation, prediction, and classification). An enormous number of scientific research publications related to bone age assessment using deep learning are explored, studied, and presented in this survey. Furthermore, the emerging trends of this research domain have been analyzed and discussed. Finally, a critical discussion section on the limitations of deep-learning models has been presented. Open research challenges and future directions in this promising area have been included as well.

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“…Iglovikov et al [5] trained several end-to-end regression models and predicted bone ages using the model ensemble strategy. In [15], the Gaussian process regression was employed to increase the sensibility to the hand pose. Wang et al [17] followed the structure of Faster-RCNN to predict bone ages.…”

Section: Introductionmentioning

confidence: 99%

Doctor Imitator: A Graph-Based Bone Age Assessment Framework Using Hand Radiographs

Chen¹,

Yu²,

Lei³

et al. 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

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Bone age assessment is challenging in clinical practice due to the complicated bone age assessment process. Current automatic bone age assessment methods were designed with rare consideration of the diagnostic logistics and thus may yield certain uninterpretable hidden states and outputs. Consequently, doctors can find it hard to cooperate with such models harmoniously because it is difficult to check the correctness of the model predictions. In this work, we propose a new graph-based deep learning framework for bone age assessment with hand radiographs, called Doctor Imitator (DI). The architecture of DI is designed to learn the diagnostic logistics of doctors using the scoring methods (e.g., the Tanner-Whitehouse method) for bone age assessment. Specifically, the convolutions of DI capture the local features of the anatomical regions of interest (ROIs) on hand radiographs and predict the ROI scores by our proposed Anatomy-based Group Convolution, summing up for bone age prediction. Besides, we develop a novel Dual Graph-based Attention module to compute patient-specific attention for ROI features and context attention for ROI scores. As far as we know, DI is the first automatic bone age assessment framework following the scoring methods without fully supervised hand radiographs. Experiments on hand radiographs with only bone age supervision verify that DI can achieve excellent performance with sparse parameters and provide more interpretability.

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Automated Assessment of Bone Age Using Deep Learning and Gaussian Process Regression

Cited by 22 publications

References 16 publications

Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks

Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks

Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

Doctor Imitator: A Graph-Based Bone Age Assessment Framework Using Hand Radiographs

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