4 Result(s)
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Chapter and Conference Paper
Dual Windows Are Significant: Learning from Mediastinal Window and Focusing on Lung Window
Since the pandemic of COVID-19, several deep learning methods were proposed to analyze the chest Computed Tomography (CT) for diagnosis. In the current situation, the disease course classification is significa...
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Chapter and Conference Paper
Class-Aware Multi-window Adversarial Lung Nodule Synthesis Conditioned on Semantic Features
Nodule CT image synthesis is effective as a data augmentation method for deep learning tasks about lung nodules. To advance the realistic malignant/benign lung nodule synthesis, the conditional Generative Adve...
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Chapter and Conference Paper
Under-Determined Blind Source Separation Anti-collision Algorithm for RFID Based on Adaptive Tree Grou**
Under-determined blind separation becomes poorer with increasing number of tags, to the point where it cannot separate source tag signals, reducing overall system performance. This paper proposes a paralleliza...
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Chapter and Conference Paper
Fine Grain Lung Nodule Diagnosis Based on CT Using 3D Convolutional Neural Network
As the core step of lung nodule analysis, lung nodule diagnosis comprises two important tasks: False Positive Reduction (FPR) and Malignancy Suspiciousness Estimation (MSE). Many studies tackle these two tasks...
