An ISHAP-based interpretation-model-guided classification method for malignant pulmonary nodule

Machine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography Scans

(2021) Rajesh P. Shah et al.   JCO Clinical Cancer Informatics

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

(2020) Alex Zwanenburg et al.   RADIOLOGY

3D-MCN: A 3D Multi-scale Capsule Network for Lung Nodule Malignancy Prediction

(2020) Parnian Afshar et al.   Scientific Reports

DICOM Re‐encoding of Volumetrically Annotated Lung Imaging Data Consortium (LIDC) Nodules

(2020) Andrey Fedorov et al.   MEDICAL PHYSICS

A review on CT image noise and its denoising

(2018) Manoj Diwakar et al.   Biomedical Signal Processing and Control

Comparison Between Radiological Semantic Features and Lung-RADS in Predicting Malignancy of Screen-Detected Lung Nodules in the National Lung Screening Trial

(2018) Qian Li et al.   Clinical Lung Cancer

Automatic benign and malignant classification of pulmonary nodules in thoracic computed tomography based on RF algorithm

(2018) Xiang-Xia Li et al.   IET Image Processing

Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining

(2018) Ryan J. Urbanowicz et al.   JOURNAL OF BIOMEDICAL INFORMATICS

Relief-Based Feature Selection: Introduction and Review

(2018) Ryan J. Urbanowicz et al.   JOURNAL OF BIOMEDICAL INFORMATICS

Radiomics and radiogenomics in lung cancer: A review for the clinician

(2018) Rajat Thawani et al.   LUNG CANCER

Explainable machine-learning predictions for the prevention of hypoxaemia during surgery

(2018) Scott M. Lundberg et al.   Nature Biomedical Engineering

The Rise of Radiomics and Implications for Oncologic Management

(2017) Vivek Verma et al.   JNCI-Journal of the National Cancer Institute

Radiomics: the bridge between medical imaging and personalized medicine

(2017) Philippe Lambin et al.   Nature Reviews Clinical Oncology

Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification

(2017) Wei Shen et al.   PATTERN RECOGNITION

Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs

(2017) Nima Tajbakhsh et al.   PATTERN RECOGNITION

3D multi-view convolutional neural networks for lung nodule classification

(2017) Guixia Kang et al.   PLoS One

Development and clinical application of radiomics in lung cancer

(2017) Bojiang Chen et al.   Radiation Oncology

Machine Learning for Medical Imaging

(2017) Bradley J. Erickson et al.   RADIOGRAPHICS

The Rise of Radiomics and Implications for Oncologic Management

(2017) Vivek Verma et al.   JNCI-Journal of the National Cancer Institute

Processor Design for Soft Errors

(2016) Tuo Li et al.   ACM COMPUTING SURVEYS

Radiomics: Images Are More than Pictures, They Are Data

(2016) Robert J. Gillies et al.   RADIOLOGY

Shape and Texture Based Novel Features for Automated Juxtapleural Nodule Detection in Lung CTs

(2015) Erdal Taşcı et al.   JOURNAL OF MEDICAL SYSTEMS

Machine Learning methods for Quantitative Radiomic Biomarkers

(2015) Chintan Parmar et al.   Scientific Reports

The Lung Reporting and Data System (LU-RADS): A Proposal for Computed Tomography Screening

(2014) Daria Manos et al.   CANADIAN ASSOCIATION OF RADIOLOGISTS JOURNAL-JOURNAL DE L ASSOCIATION CANADIENNE DES RADIOLOGISTES

Test–Retest Reproducibility Analysis of Lung CT Image Features

(2014) Yoganand Balagurunathan et al.   JOURNAL OF DIGITAL IMAGING

A survey on feature selection methods

(2013) Girish Chandrashekar et al.   COMPUTERS & ELECTRICAL ENGINEERING

SHAPE AND TEXTURE INDEXES APPLICATION TO CELL NUCLEI CLASSIFICATION

(2013) GUILLAUME THIBAULT et al.   INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE

Radiomics: Extracting more information from medical images using advanced feature analysis

(2012) Philippe Lambin et al.   EUROPEAN JOURNAL OF CANCER

Benefits and Harms of CT Screening for Lung Cancer

(2012) Peter B. Bach et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

Radiomics: the process and the challenges

(2012) Virendra Kumar et al.   MAGNETIC RESONANCE IMAGING

Recommendations for the Management of Subsolid Pulmonary Nodules Detected at CT: A Statement from the Fleischner Society

(2012) David P. Naidich et al.   RADIOLOGY

Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening

(2011)   NEW ENGLAND JOURNAL OF MEDICINE

A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification

(2009) K. Murphy et al.   MEDICAL IMAGE ANALYSIS