Zero-Shot Medical Image Retrieval for Emerging Infectious Diseases Based on Meta-Transfer Learning--Worldwide,2020

Introduction:Due to the increasing number of medical images,image retrieval has become an important technique for medical image analytics.Although many content-based image retrieval methods have been proposed,the retrieval of images in datasets related to emerging/new infectious diseases still remain a challenge-mostly due to the lack of historical data.As a result,the current retrieval models have limited functionality in helping doctors make accurate diagnoses of new diseases.Methods:In this paper,we propose a zero-shot retrieval model based on meta-learning and ensemble learning,which can obtain a model with stronger generalizability without using any relevant training data,and thus performs well on new types of test data.Results:The experimental results showed that the proposed method is 3% to 5% higher than the traditional method,which means that our model can retrieve relevant medical images more accurately for newly emerging data types and provide doctors with more effective assistance.Discussion:When a new infectious disease occurs,doctors can use the proposed zero-shot retrieval model to retrieve all relevant cases,quickly find the common problems of patients,find the locations of the new infections,and determine its infectivity as soon as possible.The proposed method is a new computeraided decision support technology for emerging infectious diseases.