Yubao Guan

Guangzhou, China

Last Updated: 09/26/2026

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Found 46 publications

Correction: Evaluation of recent lightweight deep learning architectures for lung cancer CT classification.

Correction: Evaluation of recent lightweight deep learning architectures for lung cancer CT classification.

Journal: Frontiers in oncology

Published: September 29, 2025

A CT-based deep learning approach to differentiate multiple primary lung cancers, metastases, and benign nodules.

A CT-based deep learning approach to differentiate multiple primary lung cancers, metastases, and benign nodules.

Journal: BMC cancer

Published: September 25, 2025

A Lightweight Dual-Output Vision Transformer for Enhanced Lung Nodule Classification Using CT Images.

A Lightweight Dual-Output Vision Transformer for Enhanced Lung Nodule Classification Using CT Images.

Journal: Technology in cancer research & treatment

Published: August 21, 2025

Evaluation of recent lightweight deep learning architectures for lung cancer CT classification.

Evaluation of recent lightweight deep learning architectures for lung cancer CT classification.

Journal: Frontiers in oncology

Published: June 16, 2025

Classifiers Combined with DenseNet Models for Lung Cancer Computed Tomography Image Classification: A Comparative Analysis.

Classifiers Combined with DenseNet Models for Lung Cancer Computed Tomography Image Classification: A Comparative Analysis.

Journal: Current medical imaging

Published: March 12, 2025

CT-based radiomics: A potential indicator of KRAS mutation in pulmonary adenocarcinoma.

CT-based radiomics: A potential indicator of KRAS mutation in pulmonary adenocarcinoma.

Journal: Tumori

Published: February 03, 2025

Predicting pathological subtypes of pure ground-glass nodules using Swin Transformer deep learning model.

Predicting pathological subtypes of pure ground-glass nodules using Swin Transformer deep learning model.

Journal: Insights into imaging

Published: November 08, 2024

MAEMC-NET: a hybrid self-supervised learning method for predicting the malignancy of solitary pulmonary nodules from CT images.

MAEMC-NET: a hybrid self-supervised learning method for predicting the malignancy of solitary pulmonary nodules from CT images.

Journal: Frontiers in medicine

Published: October 07, 2024

Differentiation of granulomatous nodules with lobulation and spiculation signs from solid lung adenocarcinomas using a CT deep learning model.

Differentiation of granulomatous nodules with lobulation and spiculation signs from solid lung adenocarcinomas using a CT deep learning model.

Journal: BMC cancer

Published: December 16, 2023

Application research on asthma-COPD overlap using low-dose CT scan and quantitative analysis.

Application research on asthma-COPD overlap using low-dose CT scan and quantitative analysis.

Journal: Clinical radiology

Published: November 27, 2023

Prediction of Malignancy and Pathological Types of Solid Lung Nodules on CT Scans Using a Volumetric SWIN Transformer.

Prediction of Malignancy and Pathological Types of Solid Lung Nodules on CT Scans Using a Volumetric SWIN Transformer.

Journal: Journal of imaging informatics in medicine

Published: October 24, 2023

CLSSL-ResNet: Predicting malignancy of solitary pulmonary nodules from CT images by chimeric label with self-supervised learning.

CLSSL-ResNet: Predicting malignancy of solitary pulmonary nodules from CT images by chimeric label with self-supervised learning.

Journal: Journal of X-ray science and technology

Published: July 10, 2023

Showing 1-12 of 46

Last Updated: 09/26/2026