most citedThe interpretation of endobronchial ultrasound image using 3D convolutional neural network for differentiating malignant and benign mediastinal lesions

2 citations · 3 across the 4 of their papers we have counts for

collaborators

4 papers

eess.IV2022

Computerized Tomography Pulmonary Angiography Image Simulation using Cycle Generative Adversarial Network from Chest CT imaging in Pulmonary Embolism Patients

Chia-Hung Yang, Yun-Chien Cheng, Chin Kuo

The purpose of this research is to develop a system that generates simulated computed tomography pulmonary angiography (CTPA) images clinically for pulmonary embolism diagnoses. No…

eess.IV20221 cited

Feature-enhanced Adversarial Semi-supervised Semantic Segmentation Network for Pulmonary Embolism Annotation

Ting-Wei Cheng, Jerry Chang, Ching-Chun Huang +2

This study established a feature-enhanced adversarial semi-supervised semantic segmentation model to automatically annotate pulmonary embolism lesion areas in computed tomography p…

eess.IV2022

Convolutional Neural Network for Early Pulmonary Embolism Detection via Computed Tomography Pulmonary Angiography

Ching-Yuan Yu, Ming-Che Chang, Yun-Chien Cheng +1

This study was conducted to develop a computer-aided detection (CAD) system for triaging patients with pulmonary embolism (PE). The purpose of the system was to reduce the death ra…

eess.IV20212 cited

The interpretation of endobronchial ultrasound image using 3D convolutional neural network for differentiating malignant and benign mediastinal lesions

Ching-Kai Lin, Shao-Hua Wu, Jerry Chang +1

The purpose of this study is to differentiate malignant and benign mediastinal lesions by using the three-dimensional convolutional neural network through the endobronchial ultraso…