Single-Shot Lightweight Model For The Detection of Lesions And The Prediction of COVID-19 From Chest CT Scans

Ter-Sarkisov, Aram

doi:10.1101/2020.12.01.20241786

Cited by 1 publication

(1 citation statement)

References 14 publications

(20 reference statements)

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“…d-SNE [12] introduced a new approach that exploits the stochastic neighborhood embedding theory and modified-Hausdorff distance to improve the few-shot classification performance. Although, many efforts have been done on SDA or few-shot COVID-19 diagnosis areas [13,14], applying domain adaptation on CT images for the COVID-19 diagnostic task is relatively a new area, and our proposed method is one of the first attempts in utilizing synthetic chest CT scans for fewshot COVID-19 diagnostic task.…”

Section: Introductionmentioning

confidence: 99%

Few-shot Learning for CT Scan based COVID-19 Diagnosis

Jiang¹,

Chen²,

Han³

et al. 2021

Preprint

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Coronavirus disease 2019 (COVID-19) is a Public Health Emergency of International Concern infecting more than 40 million people across 188 countries and territories. Chest computed tomography (CT) imaging technique benefits from its high diagnostic accuracy and robustness, it has become an indispensable way for COVID-19 mass testing. Recently, deep learning approaches have become an effective tool for automatic screening of medical images, and it is also being considered for COVID-19 diagnosis. However, the high infection risk involved with COVID-19 leads to relative sparseness of collected labeled data limiting the performance of such methodologies. Moreover, accurately labeling CT images require expertise of radiologists making the process expensive and time-consuming. In order to tackle the above issues, we propose a supervised domain adaption based COVID-19 CT diagnostic method which can perform effectively when only a small samples of labeled CT scans are available. To compensate for the sparseness of labeled data, the proposed method utilizes a large amount of synthetic COVID-19 CT images and adjusts the networks from the source domain (synthetic data) to the target domain (real data) with a cross-domain training mechanism. Experimental results show that the proposed method achieves state-of-theart performance on few-shot COVID-19 CT imaging based diagnostic tasks.

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Section: Introductionmentioning

confidence: 99%