Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains

Visible-Light Camera Sensor-Based Presentation Attack Detection for Face Recognition by Combining Spatial and Temporal Information

(2019) Dat Nguyen et al.   SENSORS

Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network

(2019) Lei Wang et al.   World Journal of Surgical Oncology

Pulmonary Nodule Detection in CT Scans with Equivariant CNNs

(2019) Marysia Winkels et al.   MEDICAL IMAGE ANALYSIS

Comparison between linear and nonlinear machine-learning algorithms for the classification of thyroid nodules

(2019) Fu-sheng Ouyang et al.   EUROPEAN JOURNAL OF RADIOLOGY

Ultrasound image analysis using deep learning algorithm for the diagnosis of thyroid nodules

(2019) Junho Song et al.   MEDICINE

Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study

(2019) Bach Tran et al.   Journal of Clinical Medicine

Artificial Intelligence vs. Natural Stupidity: Evaluating AI readiness for the Vietnamese Medical Information System

(2019) Quan-Hoang Vuong et al.   Journal of Clinical Medicine

Effective Diagnosis and Treatment through Content-Based Medical Image Retrieval (CBMIR) by Using Artificial Intelligence

(2019) Muhammad Owais et al.   Journal of Clinical Medicine

Super-resolution reconstruction of neonatal brain magnetic resonance images via residual structured sparse representation

(2019) Yongqin Zhang et al.   MEDICAL IMAGE ANALYSIS

Computer aided thyroid nodule detection system using medical ultrasound images

(2018) Deepika Koundal et al.   Biomedical Signal Processing and Control

Breast lesion classification based on supersonic shear-wave elastography and automated lesion segmentation from B-mode ultrasound images

(2018) Yanyan Yu et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images

(2018) U. Raghavendra et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Automatic breast ultrasound image segmentation: A survey

(2018) Min Xian et al.   PATTERN RECOGNITION

Multi-task Cascade Convolution Neural Networks for Automatic Thyroid Nodule Detection and Recognition

(2018) Wenfeng Song et al.   IEEE Journal of Biomedical and Health Informatics

Identifying Degenerative Brain Disease Using Rough Set Classifier Based on Wavelet Packet Method

(2018) Ching-Hsue Cheng et al.   Journal of Clinical Medicine

Patch-based classification of thyroid nodules in ultrasound images using direction independent features extracted by two-threshold binary decomposition

(2018) Antonin Prochazka et al.   COMPUTERIZED MEDICAL IMAGING AND GRAPHICS

Medical breast ultrasound image segmentation by machine learning

(2018) Yuan Xu et al.   ULTRASONICS

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

(2017) Shaoqing Ren et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network

(2017) Jianning Chi et al.   JOURNAL OF DIGITAL IMAGING

Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

(2017) Konstantinos Kamnitsas et al.   MEDICAL IMAGE ANALYSIS

Brain tumor segmentation with Deep Neural Networks

(2017) Mohammad Havaei et al.   MEDICAL IMAGE ANALYSIS

Spoof Detection for Finger-Vein Recognition System Using NIR Camera

(2017) Dat Tien Nguyen et al.   SENSORS

Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions

(2017) U. Raghavendra et al.   ULTRASONICS

The adaptive computer-aided diagnosis system based on tumor sizes for the classification of breast tumors detected at screening ultrasound

(2017) Woo Kyung Moon et al.   ULTRASONICS

A pre-trained convolutional neural network based method for thyroid nodule diagnosis

(2017) Jinlian Ma et al.   ULTRASONICS

Computer-aided tumor diagnosis using shear wave breast elastography

(2017) Woo Kyung Moon et al.   ULTRASONICS

ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee

(2017) Franklin N. Tessler et al.   Journal of the American College of Radiology

Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review

(2016) Vidya K Sudarshan et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Thyroid lesion classification in 242 patient population using Gabor transform features from high resolution ultrasound images

(2016) U Rajendra Acharya et al.   KNOWLEDGE-BASED SYSTEMS

Computerized analysis of calcification of thyroid nodules as visualized by ultrasonography

(2015) Woo Jung Choi et al.   EUROPEAN JOURNAL OF RADIOLOGY

Deep convolutional neural networks for multi-modality isointense infant brain image segmentation

(2015) Wenlu Zhang et al.   NEUROIMAGE

Who could benefit the most from using a computer-aided detection system in full-field digital mammography?

(2014) Na Jung et al.   World Journal of Surgical Oncology

ThyroScreen system: High resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform

(2011) U. Rajendra Acharya et al.   COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

Non-invasive automated 3D thyroid lesion classification in ultrasound: A class of ThyroScan™ systems

(2011) U. Rajendra Acharya et al.   ULTRASONICS

Breast Tumor Classification Using Fuzzy Clustering for Breast Elastography

(2011) Woo Kyung Moon et al.   ULTRASOUND IN MEDICINE AND BIOLOGY

Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images

(2010) Chuan-Yu Chang et al.   PATTERN RECOGNITION

Research and Statistics: Sensitivity, Specificity, Predictive Values, and Likelihood Ratios

(2010) D. N. Carvajal et al.   PEDIATRICS IN REVIEW

Thyroid nodule classification using ultrasound elastography via linear discriminant analysis

(2010) Si Luo et al.   ULTRASONICS