DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19

Early detection of the coronavirus disease 2019 (COVID-19) helps to treat patients timely and increase the cure rate, thus further suppressing the spread of the disease. In this study, we propose a novel deep learning based detection and similar case recommendation network to help control the epidemic. Our proposed network contains two stages: the first one is a lung region segmentation step and is used to exclude irrelevant factors, and the second is a detection and recommendation stage. Under this framework, in the second stage, we develop a dual-children network (DuCN) based on a pre-trained ResNet-18 to simultaneously realize the disease diagnosis and similar case recommendation. Besides, we employ triplet loss and intrapulmonary distance maps to assist the detection, which helps incorporate tiny differences between two images and is conducive to improving the diagnostic accuracy. For each confirmed COVID-19 case, we give similar cases to provide radiologists with diagnosis and treatment references. We conduct experiments on a large publicly available dataset (CC-CCII) and compare the proposed model with state-of-the-art COVID-19 detection methods. The results show that our proposed model achieves a promising clinical performance. © 2021, Springer Nature Switzerland AG.

[1]  Fatemeh Homayounieh,et al.  CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image , 2020, ArXiv.

[2]  Milan Sonka,et al.  Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning , 2020, Medical Image Analysis.

[3]  Raymond Y Huang,et al.  AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT , 2020, Radiology.

[4]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[5]  Jianjiang Feng,et al.  Development and evaluation of an artificial intelligence system for COVID-19 diagnosis , 2020, Nature Communications.

[6]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Ezz El-Din Hemdan,et al.  COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images , 2020, ArXiv.

[8]  M. Mildner,et al.  Re-epithelialization and immune cell behaviour in an ex vivo human skin model , 2020, Scientific Reports.

[9]  W. Liang,et al.  Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography , 2020, Cell.

[10]  Cheng Chen,et al.  COVID-AL: The diagnosis of COVID-19 with deep active learning , 2020, Medical Image Analysis.

[11]  Liang Zheng,et al.  Thorax disease classification with attention guided convolutional neural network , 2020, Pattern Recognit. Lett..

[12]  K. Cao,et al.  Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy , 2020 .

[13]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[14]  Alexander Wong,et al.  COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images , 2020, Scientific reports.

[15]  Kang Zhang,et al.  KISEG: A Three-Stage Segmentation Framework for Multi-level Acceleration of Chest CT Scans from COVID-19 Patients , 2020, MICCAI.

[16]  Bin Li,et al.  Dense networks with relative location awareness for thorax disease identification. , 2019, Medical physics.

[17]  Yaozong Gao,et al.  Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia , 2020, IEEE Transactions on Medical Imaging.

[18]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Zhanju Liu,et al.  Clinical Features of COVID-19-Related Liver Functional Abnormality , 2020, Clinical Gastroenterology and Hepatology.

[20]  Li Chen,et al.  COVID-19 CT Lung and Infection Segmentation Dataset , 2020 .

[21]  Le Lu,et al.  Thorax-Net: An Attention Regularized Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography , 2020, IEEE Journal of Biomedical and Health Informatics.

[22]  Kai Ma,et al.  Efficient and Effective Training of COVID-19 Classification Networks With Self-Supervised Dual-Track Learning to Rank , 2020, IEEE Journal of Biomedical and Health Informatics.

[23]  X. He,et al.  Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans , 2020, medRxiv.

[24]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[25]  Hui Shen,et al.  Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images , 2020, Medical Image Analysis.

[26]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Wenyu Liu,et al.  Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label , 2020, medRxiv.

[28]  Chao Di,et al.  U1 snRNP regulates cancer cell migration and invasion in vitro , 2020, Nature Communications.

[29]  Wenyu Liu,et al.  A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT , 2020, IEEE Transactions on Medical Imaging.

[30]  Yuan Gao,et al.  Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images , 2020, IEEE Access.

[31]  Shaohuai Shi,et al.  Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans , 2021, AAAI.