Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence

Nguyen, Dat Tien; Kang, Jin Kyu; Pham, Tuyen Danh; Batchuluun, Ganbayar; Park, Kang Ryoung

doi:10.3390/s20071822

Cited by 88 publications

(45 citation statements)

References 52 publications

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“…To measure the performance of a CAD system, previous studies used three popular metrics: Sensitivity, specificity, and overall accuracy [ 37 , 38 ]. These metrics are used to measure three different aspects of a binary classification system, i.e., the classification/detection ability of the system with respect to the positive (with the appearance of disease), negative (without the appearance of disease), and overall cases.…”

Section: Proposed Methodsmentioning

confidence: 99%

Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models

Nguyen

Lee

Pham

et al. 2020

Sensors

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In vivo diseases such as colorectal cancer and gastric cancer are increasingly occurring in humans. These are two of the most common types of cancer that cause death worldwide. Therefore, the early detection and treatment of these types of cancer are crucial for saving lives. With the advances in technology and image processing techniques, computer-aided diagnosis (CAD) systems have been developed and applied in several medical systems to assist doctors in diagnosing diseases using imaging technology. In this study, we propose a CAD method to preclassify the in vivo endoscopic images into negative (images without evidence of a disease) and positive (images that possibly include pathological sites such as a polyp or suspected regions including complex vascular information) cases. The goal of our study is to assist doctors to focus on the positive frames of endoscopic sequence rather than the negative frames. Consequently, we can help in enhancing the performance and mitigating the efforts of doctors in the diagnosis procedure. Although previous studies were conducted to solve this problem, they were mostly based on a single classification model, thus limiting the classification performance. Thus, we propose the use of multiple classification models based on ensemble learning techniques to enhance the performance of pathological site classification. Through experiments with an open database, we confirmed that the ensemble of multiple deep learning-based models with different network architectures is more efficient for enhancing the performance of pathological site classification using a CAD system as compared to the state-of-the-art methods.

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Section: Proposed Methodsmentioning

confidence: 99%

Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models

Nguyen

Lee

Pham

et al. 2020

Sensors

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“…Various features are used in ultrasound images, and as shown in Table 2 , many CAD studies have been conducted recently using deep learning. Nguyen et al [ 60 ] introduced a method of combining ResNet50-based CNN architecture and Inception-based CNN architecture with a weighted binary cross-entropy loss function. Park et al [ 61 ] integrated seven ultrasound features (composition, echogenicity, orientation, margin, spongiform, shape, and calcification) and compared the performance with those of a support vector machine-based ultrasound CAD system and radiologists.…”

Section: Diagnostic Support By Deep Learning Analyticsmentioning

confidence: 99%

Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency

Kang

Kwon

et al. 2021

Ultrasonography

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Reviews of the most recent applications of deep learning on ultrasound imaging applications are presented. Architectures of deep learning networks are briefly explained for medical imaging application categories of classification, detection, segmentation, and generation. Ultrasonography applications are then reviewed and summarized for image processing and diagnosis along with some representative study cases of breast, thyroid, heart, kidney, liver, and fetal head. Efforts on workflow enhancement are also reviewed with emphasis on view recognition, scanning guide, image quality assessment, and quantification and measurement. Finally some future prospects are presented on image quality Enhancement, diagnostic support, and improving workflow efficiency, along with remarks on hurdles, benefits, and necessary collaborations.

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“…Conventional methods describe features by manually designing feature descriptors. In recent years, with the development of deep learning [8], [9], methods based on deep convolutional networks can learn more discriminative features than manual features. In [10], [11], a ResNet-based network was proposed to extract the global features of the whole body and to measure the similarity of the extracted features.…”

Section: Related Workmentioning

confidence: 99%

“…On the CUHK03 dataset, our method can also obtain a good result as compared with some classic methods. 9 4.12×10 7 without Attention 4.58×10 9 3.20×10 7 with Attention 4.73×10 9 3.28×10 7…”

Section: Experimental Analysismentioning

confidence: 99%

Deep Learning Research With an Expectation-Maximization Model for Person Re-Identification

2020

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In existing person re-identification methods based on deep learning, the extraction of good features is still a key step. Some efforts divide the image of a person into multiple parts to extract more detailed information from semantically coherent parts but ignore their correlation with each other. Others adopt self attention to reallocate weights of pixels for learning the association between different regions. This association can improve the accuracy of the person re-identification task, but the features obtained by this type of algorithm have high redundancy, which is not conducive to the expression of feature information. In order to address the above challenges, we propose a feature extraction method based on a novel attention mechanism which combines the expectation maximization (EM) algorithm and non-local operation. We embed the attention module into the ResNet50 backbone network. The attention module captures the correlation between different regional features through non-local operation and then reconstructs these features through the EM algorithm. In addition, we divide the network into a global branch and a local branch, where the global branch extracts the complete features, and the local branch uses the Batch DropBlock method to erase a portion of the features to achieve feature diversity. Finally, extensive experiments validate the superiority of the proposed model for person re-ID over a wide variety of state-of-the-art methods on three large-scale benchmarks, including DukeMTMC-ReID, Market-1501 and CUHK03.

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Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence

Cited by 88 publications

References 52 publications

Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models

Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models

Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency

Deep Learning Research With an Expectation-Maximization Model for Person Re-Identification

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