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Identifying Suspicious Regions of Covid-19 by Abnormality-Sensitive Activation Mapping
This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes.
COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty
We utilize the scale uncertainty among various receptive field sizes of a segmentation FCN to obtain infection regions.
Lung infection and normal region segmentation from CT volumes of COVID-19 cases
Our method recognizes and segments lung normal and infection regions in CT volumes.
Semantic Segmentation of Thigh Muscle using 2.5D Deep Learning Network Trained with Limited Datasets
Purpose: We propose a 2. 5D deep learning neural network (DLNN) to automatically classify thigh muscle into 11 classes and evaluate its classification accuracy over 2D and 3D DLNN when trained with limited datasets.
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