DCSegNet: Deep Learning Framework Based on Divide-and-Conquer Method for Liver Segmentation

Li, Congsheng; Yao, Guorong; Xu, Xu; Yang, Lei; Zhang, Yi; Wu, Tongning; Sun, Jun‐Hui

doi:10.1109/access.2020.3012990

Cited by 13 publications

(4 citation statements)

References 26 publications

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“…Although 2D CNN has been demonstrated to perform effectively in tasks requiring the classification of heart sounds, they need further feature transformations. Several CNN architectures have been proposed for 1D CNN‐based methods to identify abnormal heart sounds, including in studies such as Fakhry and Brery (2022), Krishnan et al (2020), and Li, Yao, et al (2020). In a typical example, as demonstrated in Zeng et al (2023), raw data is utilized as input to a 1D CNN without additional transformations.…”

Section: Deep Learning For Heart Sound Classificationmentioning

confidence: 99%

Artificial intelligence for heart sound classification: A review

Chen,

Guo,

et al. 2024

Expert Systems

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Heart sound signal analysis is very important for the early identification and treatment of cardiovascular illness. With rapid advancements in science and technology, artificial intelligence technologies are providing tremendous opportunities to enhance diagnosis and clinical decision‐making. Instruments can now perform clinical diagnoses that previously could only be handled by human experts more conveniently and efficiently. Despite multiple works on automatic heart sound analysis, there are few summarization and review works. This article attempts to give a thorough overview of various heart sound analysis subtasks and examine the improvements made in each subtask by both machine learning techniques and deep learning algorithms. It goals to highlight the potential of AI to revolutionize cardiovascular healthcare by enabling accurate and automated analysis of heart sounds. The findings of this review are beneficial for researchers, clinicians, and engineers in the development and application of AI‐based solutions for improved heart sound classification and diagnosis.

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Section: Deep Learning For Heart Sound Classificationmentioning

confidence: 99%

Artificial intelligence for heart sound classification: A review

Chen,

Guo,

et al. 2024

Expert Systems

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“…In recent years, the development and application of deep learning techniques based on artificial neural networks has increased rapidly. Deep learning techniques have particularly exhibited superior results in medical image analysis (6)(7)(8)(9)(10)(11)(12). The present study established a novel, fully automated SMA method based on the deep learning algorithm that automated the entire process of the TW3 (both of RUS-series and C-series) BAA method.…”

Section: Introductionmentioning

confidence: 98%

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

Zhang

Zhu

et al. 2022

Quant Imaging Med Surg

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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 algorithms based on deep learning have displayed the following deficiencies: (I) most methods involve end-to-end prediction, lacking integration with clinically interpretable methods; (II) BAA methods exhibit racial and geographical differences.Methods: A novel, automatic skeletal maturity assessment (SMA) method with clinically interpretable methods was proposed based on a multi-region ensemble of convolutional neural networks (CNNs). This method predicted skeletal maturity scores and thus assessed bone age by utilizing left-hand radiographs and key regional patches of clinical concern.Results: Experiments included 4,861 left-hand radiographs from the database of Beijing Jishuitan Hospital and revealed that the mean absolute error (MAE) was 31.4±0.19 points (skeletal maturity scores) and 0.45±0.13 years (bone age) for the carpal bones-series and 29.9±0.21 points and 0.43±0.17 years, respectively, for the radius, ulna, and short (RUS) bones series based on the Tanner-Whitehouse 3 (TW3) method. Conclusions:The proposed automatic SMA method, which was without racial and geographical influence, is a novel, automatic method for assessing childhood bone development by utilizing skeletal maturity.Furthermore, it provides a comparable performance to endocrinologists, with greater stability and efficiency.

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“…These techniques have demonstrated exceptional performance in medical image analysis. [7][8][9][10][11][12][13][14][15][16] Generally, deep learning-based approaches for BAA involve initial extraction of TW3-ROIs from radiographs, followed by bone age evaluation through training classification models. However, the clinical adoption of relevant algorithms is hindered by significant morphological differences in TW3-ROIs during various stages of skeletal development and the overlapping interbone occlusions.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, there has been a remarkable advancement and application of deep learning techniques based on artificial neural networks. These techniques have demonstrated exceptional performance in medical image analysis 7–16 . Generally, deep learning‐based approaches for BAA involve initial extraction of TW3‐ROIs from radiographs, followed by bone age evaluation through training classification models.…”

Section: Introductionmentioning

confidence: 99%

SVTNet: Automatic bone age assessment network based on TW3 method and vision transformer

Wu,

Mi,

Zhang

et al. 2023

Int J Imaging Syst Tech

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This study aims to develop a proficient and clinically applicable algorithm that can accurately assess bone age. This algorithm is based on the principles of the Tanner‐Whitehouse 3 (TW3) integral approach, and aims to achieve efficiency, scalability, and interpretability. We developed a model for bone age prediction in children. The model was tested on a pediatric dataset from a tertiary care hospital consisting of left‐hand radiographs of children between the age of 0 and 18. Our model consists of removing the arm portion using a pre‐trained YOLO network, localizing 37 key points in the hand bone portion using a spatial configuration network, and segmenting the original image through 20 of these points to obtain 20 fixed‐size patches. Finally, each of the 20 bone images is classified by training a visual transformer (ViT) model. In this study, a hybrid network, SVTNet, was developed that incorporates visual transformers to obtain estimates of bone age in the carpal (C series) and metacarpal (RUS series) bones. The sum of the clinical TW3 scoring region scores and bone maturity scores were utilized to determine the bone age for each corresponding region. The performance of the algorithm was evaluated in terms of both training and testing by evaluating 3871 left hand X‐ray micrographs obtained from a tertiary hospital in China. The results showed that the average absolute error of bone age estimation was 0.50 years for the RUS series of bones and 0.47 years for the C series of bones. The main contribution of this study is to propose, for the first time, a ViT‐based bone age assessment method that automates the entire process of the TW3 algorithm and is clinically interpretable, with predictive accuracy comparable to that of an experienced orthopedic surgeon.

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DCSegNet: Deep Learning Framework Based on Divide-and-Conquer Method for Liver Segmentation

Cited by 13 publications

References 26 publications

Artificial intelligence for heart sound classification: A review

Artificial intelligence for heart sound classification: A review

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

SVTNet: Automatic bone age assessment network based on TW3 method and vision transformer

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