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 aim 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. The proposed model utilizes multi-similarity loss with hard-mining sampling strategy and attention mechanism to learn the optimized embedding space, and provides similar images, the visualizations of disease-related attention maps and useful clinical information to assist clinical decisions. 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 for COVID-19, where the pre-trained model is applied to extract image features from a new dataset without any further training. The extracted features are then combined with COVID-19 patient's vitals, lab tests and medical histories to predict the possibility of airway intubation in 72 hours, which is strongly associated with patient prognosis, and is crucial for patient care and hospital resource planning. 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.

Attention-based Dynamic Subspace Learners for Medical Image Analysis

“…In [47], DML is used to pre-train a model in the application of digital pathology classification, where authors use a ProxyNCA loss for learning transferable features. To enhance the embedding, [48], [49] has integrated a multi-similarity loss to DML in the context of chest radiography and liver histopathology image, respectively. Nevertheless, most of these methods are developed with the goal of classification tasks and do not effectively leverage the geometrical information of the underlying embedding space.…”

Recovery of Natural Scenery Image by Content Using Wiener-Granger Causality: A Self-Organizing Methodology

“…Deep metric learning-based image retrieval system for chest radiography and its clinical applications in COVID-19 [33].…”

OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System

“…[15] developed a chest X-ray image retrieval system for COVID-19 detection with deep denoising autoencoders as feature extractors. [26] designed an image retrieval system for COVID-19 chest radiograph via optimizing a multi-similarity loss. Outside medical domain, methods including FastAP [3], MultiSimilarity [25], CircleLoss [22] and SupCon [14] try to discover challenging negative data to improve the retrieval accuracy.…”