One deep learning local-global model based on CT imaging to differentiate between nodular cryptococcosis and lung cancer which are hard to be diagnosed

Distant Domain Transfer Learning for Medical Imaging

(2021) Shuteng Niu et al.   IEEE Journal of Biomedical and Health Informatics

External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules

(2020) David R Baldwin et al.   THORAX

Clinical analysis in immunocompetent and immunocompromised patients with pulmonary cryptococcosis in western China

(2020) Junyan Qu et al.   Scientific Reports

3D Deep Learning on Medical Images: A Review

(2020) Satya P. Singh et al.   SENSORS

Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology

(2019) Wenting Tu et al.   LUNG CANCER

Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging

(2019) Yiwen Xu et al.   CLINICAL CANCER RESEARCH

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

(2019) Diego Ardila et al.   NATURE MEDICINE

A radiomics model for determining the invasiveness of solitary pulmonary nodules that manifest as part-solid nodules

(2019) Q. Weng et al.   CLINICAL RADIOLOGY

Clinical and microbiological characteristics of cryptococcosis at an university hospital in China from 2013 to 2017

(2019) Lan-fang Fang et al.   Brazilian Journal of Infectious Diseases

Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body

(2019) Chenchen Pan et al.   CELL

A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images

(2019) Jing Gong et al.   EUROPEAN RADIOLOGY

Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer

(2019) Mengdi Cong et al.   LUNG CANCER

Survey on deep learning for pulmonary medical imaging

(2019) Jiechao Ma et al.   Frontiers of Medicine

Comparison of Clinical Features and Prognostic Factors of Cryptococcal Meningitis Caused by Cryptococcus neoformans in Patients With and Without Pulmonary Nodules

(2018) Wenhao Cao et al.   MYCOPATHOLOGIA

Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

(2018) Freddie Bray et al.   CA-A CANCER JOURNAL FOR CLINICIANS

CT-based radiomics signature for differentiating solitary granulomatous nodules from solid lung adenocarcinoma

(2018) Xinguan Yang et al.   LUNG CANCER

3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas

(2018) Wei Zhao et al.   CANCER RESEARCH

Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas

(2018) Niha Beig et al.   RADIOLOGY

Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study

(2018) Ahmed Hosny et al.   PLOS MEDICINE

Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT

(2018) Yutong Xie et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

A Lightweight Multi-Section CNN for Lung Nodule Classification and Malignancy Estimation

(2018) Pranjal Sahu et al.   IEEE Journal of Biomedical and Health Informatics

An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT

(2017) Mehdi Alilou et al.   MEDICAL PHYSICS

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

(2017) Guixia Kang et al.   PLoS One

Radiomics: Images Are More than Pictures, They Are Data

(2016) Robert J. Gillies et al.   RADIOLOGY

Epidemiology of Cryptococcus and cryptococcosis in China

(2015) Wei Fang et al.   FUNGAL GENETICS AND BIOLOGY

Clinical analysis of 76 patients pathologically diagnosed with pulmonary cryptococcosis

(2012) Yuan Zhang et al.   EUROPEAN RESPIRATORY JOURNAL