Semantic-Powered Explainable Model-Free Few-Shot Learning Scheme of Diagnosing COVID-19 on Chest X-Ray

2019年冠状病毒病(COVID-19) 弹丸 方案(数学) 计算机科学 人工智能 严重急性呼吸综合征冠状病毒2型(SARS-CoV-2) 2019-20冠状病毒爆发 放射科 医学 内科学 病毒学 数学 材料科学 数学分析 疾病 传染病(医学专业) 爆发 冶金
作者
Yihang Wang,Chunjuan Jiang,Youqing Wu,Tianxu Lv,Heng Sun,Yuan Liu,Lihua Li,Xiang Pan
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:26 (12): 5870-5882 被引量:10
标识
DOI:10.1109/jbhi.2022.3205167
摘要

Chest X-ray (CXR) is commonly performed as an initial investigation in COVID-19, whose fast and accurate diagnosis is critical. Recently, deep learning has a great potential in detecting people who are suspected to be infected with COVID-19. However, deep learning resulting with black-box models, which often breaks down when forced to make predictions about data for which limited supervised information is available and lack inter-pretability, still is a major barrier for clinical integration. In this work, we hereby propose a semantic-powered explainable model-free few-shot learning scheme to quickly and precisely diagnose COVID-19 with higher reliability and transparency. Specifically, we design a Report Image Explanation Cell (RIEC) to exploit clinically indicators derived from radiology reports as interpretable driver to introduce prior knowledge at training. Meanwhile, multi-task collaborative diagnosis strategy (MCDS) is developed to construct N-way K-shot tasks, which adopts a cyclic and collaborative training approach for producing better generalization performance on new tasks. Extensive experiments demonstrate that the proposed scheme achieves competitive results (accuracy of 98.91%, precision of 98.95%, recall of 97.94% and F1-score of 98.57%) to diagnose COVID-19 and other pneumonia infected categories, even with only 200 paired CXR images and radiology reports for training. Furthermore, statistical results of comparative experiments show that our scheme provides an interpretable window into the COVID-19 diagnosis to improve the performance of the small sample size, the reliability and transparency of black-box deep learning models. Our source codes will be released on https://github.com/AI-medical-diagnosis-team-of-JNU/SPEMFSL-Diagnosis-COVID-19.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
fcyyc发布了新的文献求助10
刚刚
yulong发布了新的文献求助10
1秒前
完美世界应助jiao采纳,获得10
1秒前
同人一剑完成签到,获得积分10
2秒前
QIU完成签到,获得积分20
2秒前
3秒前
Hello应助布洛芬采纳,获得10
3秒前
4秒前
4秒前
5秒前
6秒前
万能图书馆应助迟到虞姬采纳,获得10
8秒前
潇洒飞丹发布了新的文献求助10
8秒前
wwpedd给liaodongjun的求助进行了留言
8秒前
9秒前
9秒前
lalala发布了新的文献求助10
11秒前
1111111发布了新的文献求助10
11秒前
zjfmmu完成签到,获得积分10
12秒前
12秒前
壮观寒荷完成签到,获得积分10
14秒前
哦啦啦发布了新的文献求助10
14秒前
布洛芬发布了新的文献求助10
15秒前
执着新蕾发布了新的文献求助10
15秒前
21完成签到,获得积分10
18秒前
18秒前
19秒前
万能图书馆应助壮观寒荷采纳,获得10
20秒前
杳鸢应助21采纳,获得10
21秒前
可爱的函函应助温温采纳,获得10
21秒前
junzhu完成签到,获得积分10
21秒前
Catalina_S完成签到,获得积分0
21秒前
wanci应助郝出站采纳,获得30
21秒前
Ava应助小花生zz采纳,获得30
22秒前
闵卷完成签到,获得积分10
22秒前
萝卜头完成签到,获得积分10
22秒前
0526Test完成签到 ,获得积分10
23秒前
23秒前
李健的小迷弟应助RUSTY采纳,获得10
23秒前
23秒前
高分求助中
The Mother of All Tableaux Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 2400
Ophthalmic Equipment Market by Devices(surgical: vitreorentinal,IOLs,OVDs,contact lens,RGP lens,backflush,diagnostic&monitoring:OCT,actorefractor,keratometer,tonometer,ophthalmoscpe,OVD), End User,Buying Criteria-Global Forecast to2029 2000
Optimal Transport: A Comprehensive Introduction to Modeling, Analysis, Simulation, Applications 800
Official Methods of Analysis of AOAC INTERNATIONAL 600
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 588
T/CIET 1202-2025 可吸收再生氧化纤维素止血材料 500
Comparison of adverse drug reactions of heparin and its derivates in the European Economic Area based on data from EudraVigilance between 2017 and 2021 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3952383
求助须知:如何正确求助?哪些是违规求助? 3497737
关于积分的说明 11088744
捐赠科研通 3228363
什么是DOI,文献DOI怎么找? 1784838
邀请新用户注册赠送积分活动 868913
科研通“疑难数据库(出版商)”最低求助积分说明 801303