Deep Metric Learning-based Image Retrieval System for Chest Radiograph and its Clinical Applications in COVID-19

Abstract

In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public health crisis of the novel coronavirus disease 2019 (COVID-19) pandemic. Chest radiograph (CXR) has been playing a crucial role in COVID-19 patient triaging, diagnosing and monitoring, particularly in the United States. Considering the mixed and unspecific signals in CXR, an image retrieval model of CXR that provides both similar images and associated clinical information can be more clinically meaningful than a direct image diagnostic model. In this work we develop a novel CXR image retrieval model based on deep metric learning. Unlike traditional diagnostic models which aims at learning the direct mapping from images to labels, the proposed model aims at learning the optimized embedding space of images, where images with the same labels and similar contents are pulled together. It utilizes multi-similarity loss with hard-mining sampling strategy and attention mechanism to learn the optimized embedding space, and provides similar images to the query image. The model is trained and validated on an international multi-site COVID-19 dataset collected from 3 different sources. Experimental results of COVID-19 image retrieval and diagnosis tasks show that the proposed model can serve as a robust solution for CXR analysis and patient management for COVID-19. The model is also tested on its transferability on a different clinical decision support task, where the pre-trained model is applied to extract image features from a new dataset without any further training. These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.

Aoxiao Zhong and Xiang Li contribute equally to this work

References in corpus (12)

• A Survey on Deep Learning in Medical Image Analysis
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• Representation Learning with Contrastive Predictive Coding
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks
• Medical Image Retrieval using Deep Convolutional Neural Network
• Residual Attention Network for Image Classification
• Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning
• Learning to detect chest radiographs containing lung nodules using visual attention networks
• Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning
• Deep & Cross Network for Ad Click Predictions
• Attention-based Ensemble for Deep Metric Learning

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