A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images.

ABSTRACT: Recent studies indicate that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 identification. In this paper, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia-infected area segmentation in CT images for the detection of COVID-19. Traditional methods for CT scan segmentation exploit a supervised learning paradigm, so they (a) require large volumes of data for their training, and (b) assume fixed (static) network weights once the training procedure has been completed. Recently, to overcome these difficulties, few-shot learning (FSL) has been introduced as a general concept of network model training using a very small amount of samples. In this paper, we explore the efficacy of few-shot learn

SUBMITTER: Voulodimos A 

PROVIDER: S-EPMC8004971 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature