2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDCOVID-IP-1.8
Paper Title EXPLOITING DEEP CROSS-SLICE FEATURES FROM CT IMAGES FOR MULTI-CLASS PNEUMONIA CLASSIFICATION
Authors Jiawang Cao, Lulu Jiang, Junlin Hou, Longquan Jiang, Ruiwei Zhao, Weiya Shi, Fei Shan, Rui Feng, Fudan University, China
SessionCOVID-IP-1: COVID Related Image Processing 1
LocationArea C
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic COVID-Related Image Processing: COVID-related image processing
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Abstract Computed Tomography (CT) scanning is widely used for chest diseases detection including pneumonia due to its diagnostic efficacy and efficiency. Recent studies have shown that the distribution of infection regions caused by COVID-19 in CT images is different from other pneumonia, and the COVID-19 cases are more likely to suffer severe and large-area infections. However, most deep learning methods only focus on intra-slice features and ignore cross-slice features. In this paper, we propose a novel two-stage method to fully exploit deep cross-slice features from volumetric CT data, including a dual-task supervised CNN and a context-aware Bi-LSTM. To further demonstrate the effectiveness of our model, we conduct extensive experiments on a chest CT imaging dataset with a total of 801 patients (250 healthy people, 238 COVID-19 patients, 191 H1N1 patients, and 122 CAP patients). The experimental results indicate the superiority of our proposed model on the multi-class pneumonia classification task.