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An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

Guney G, Yigin BO, Guven N, Alici YH, Colak B, Erzin G, Saygili G

Deep learning (DL) algorithms have achieved important successes in data analysis tasks, thanks to their capability of revealing complex patterns in data. With the advance of new sensors, data storage,...
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Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment

Lee BD, Lee MS

Bone age assessments are a complicated and lengthy process, which are prone to inter- and intra-observer variabilities. Despite the great demand for fully automated systems, developing an accurate and robust...
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Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm

Kim KO, Kim EY

Endoscpists always have tried to pursue a perfect colonoscopy, and application of artificial intelligence (AI) using deep-learning algorithms is one of the promising supportive options for detection and characterization of...
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Artificial intelligence in breast ultrasonography

Kim J, Kim HJ, Kim C, Kim WH

Although breast ultrasonography is the mainstay modality for differentiating between benign and malignant breast masses, it has intrinsic problems with false positives and substantial interobserver variability. Artificial intelligence (AI), particularly...
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Evaluation of a multi-stage convolutional neural network-based fully automated landmark identification system using cone-beam computed tomographysynthesized posteroanterior cephalometric images

Kim MJ, Liu Y, Oh SH, Ahn HW, Kim SH, Nelson G

Objective: To evaluate the accuracy of a multi-stage convolutional neural network (CNN) model-based automated identification system for posteroanterior (PA) cephalometric landmarks. Methods: The multi-stage CNN model was implemented with a...
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Content-Based Image Retrieval of Chest CT with Convolutional Neural Network for Diffuse Interstitial Lung Disease: Performance Assessment in Three Major Idiopathic Interstitial Pneumonias

Hwang HJ, Seo JB, Lee SM, Kim EY, Park B, Bae HJ, Kim N

Objective: To assess the performance of content-based image retrieval (CBIR) of chest CT for diffuse interstitial lung disease (DILD). Materials and Methods: The database was comprised by 246 pairs of chest...
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Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency

Yi J, Kang HK, Kwon JH, Kim KS, Park MH, Seong YK, Kim DW, Ahn B, Ha K, Lee J, Hah Z, Bang WC

In this review of the most recent applications of deep learning to ultrasound imaging, the architectures of deep learning networks are briefly explained for the medical imaging applications of classification,...
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Convolutional Neural Network Based Sinogram Extrapolation for Truncated CT: Preliminary Study

Park YI, Suh TS

  • KMID: 2510116
  • Prog Med Phys.
  • 2020 Sep;31(S1):S71.
Purpose: Evaluate the feasibility of the convolutional neural network based sinogram extrapolation method to extend the field-of-view in truncated computed tomography (CT). Methods: Initial sinogram datasets were collected using forward projections...
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The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements

Yang YJ

Capsule endoscopy has revolutionized the management of small-bowel diseases owing to its convenience and noninvasiveness. Capsule endoscopy is a common method for the evaluation of obscure gastrointestinal bleeding, Crohn’s disease,...
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Automatic Detection and Classification of Rib Fractures on Thoracic CT Using Convolutional Neural Network: Accuracy and Feasibility

Zhou QQ, Wang J, Tang W, Hu ZC, Xia ZY, Li XS, Zhang R, Yin X, Zhang B, Zhang H

Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and...
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Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer

Yoon HJ, Kim JH

Diagnosis and evaluation of early gastric cancer (EGC) using endoscopic images is significantly important; however, it has some limitations. In several studies, the application of convolutional neural network (CNN) greatly...
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Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy

Choi J, Shin K, Jung J, Bae HJ, Kim DH, Byeon JS, Kim N

Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at...
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Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG

Urtnasan E, Park JU, Joo EY, Lee KJ

Background: This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal. Methods: A convolutional neural network (CNN)...
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Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning

Do S, Song KD, Chung JW

Artificial intelligence has been applied to many industries, including medicine. Among the various techniques in artificial intelligence, deep learning has attained the highest popularity in medical imaging in recent years....
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Feasibility of fully automated classification of whole slide images based on deep learning

Cho KO, Lee SH, Jang HJ

Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners...
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Fully Automatic Segmentation of Acute Ischemic Lesions on Diffusion-Weighted Imaging Using Convolutional Neural Networks: Comparison with Conventional Algorithms

Woo I, Lee A, Jung SC, Lee H, Kim N, Cho SJ, Kim D, Lee J, Sunwoo L, Kang DW

OBJECTIVE: To develop algorithms using convolutional neural networks (CNNs) for automatic segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) and compare them with conventional algorithms, including a thresholding-based segmentation. MATERIALS...
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Overview of Deep Learning in Gastrointestinal Endoscopy

Min JK, Kwak MS, Cha JM

Artificial intelligence is likely to perform several roles currently performed by humans, and the adoption of artificial intelligence-based medicine in gastroenterology practice is expected in the near future. Medical image-based...
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Medical Image Analysis Using Artificial Intelligence

Yoon HJ, Jeong YJ, Kang H, Jeong JE, Kang DY

PURPOSE: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data....
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Development of Predictive Models in Patients with Epiphora Using Lacrimal Scintigraphy and Machine Learning

Park YJ, Bae JH, Shin MH, Hyun SH, Cho YS, Choe YS, Choi JY, Lee KH, Kim BT, Moon SH

PURPOSE: We developed predictive models using different programming languages and different computing platforms for machine learning (ML) and deep learning (DL) that classify clinical diagnoses in patients with epiphora. We...
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Development of Artificial Intelligence to Support Needle Electromyography Diagnostic Analysis

Nam S, Sohn MK, Kim HA, Kong HJ, Jung IY

OBJECTIVES: This study proposes a method for classifying three types of resting membrane potential signals obtained as images through diagnostic needle electromyography (EMG) using TensorFlow-Slim and Python to implement an...
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