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Detecting Pulmonary Embolism from Computed Tomography Using Convolutional Neural Network
The clinical symptoms of pulmonary embolism (PE) are very diverse and non-specific, which makes it difficult to diagnose.
Computerized Tomography Pulmonary Angiography Image Simulation using Cycle Generative Adversarial Network from Chest CT imaging in Pulmonary Embolism Patients
This study is expected to propose a new approach to the clinical diagnosis of pulmonary embolism, in which a deep learning network is used to assist in the complex screening process and to review the generated simulated CTPA images, allowing physicians to assess whether a patient needs to undergo detailed testing for CTPA, improving the speed of detection of pulmonary embolism and significantly reducing the number of undetected patients.
Feature-enhanced Adversarial Semi-supervised Semantic Segmentation Network for Pulmonary Embolism Annotation
By training the model with both labeled and unlabeled images, the accuracy of unlabeled images can be improved and the labeling cost can be reduced.
Convolutional Neural Network for Early Pulmonary Embolism Detection via Computed Tomography Pulmonary Angiography
This study was conducted to develop a computer-aided detection (CAD) system for triaging patients with pulmonary embolism (PE).
