Abstract
With the global pandemic of infectious diseases, the demand for accurate nucleic acid detection is daily increasing. The traditional threshold-based algorithms are adopted as the mainstream for processing the images of digital polymerase chain reaction (dPCR) now, but they are facing huge challenges on complex problems such as irregular noise, uneven illumination, and the lack of data. So, this paper proposed a novel few-shot learning method based on our improved YOLOv3 model with fast processing speed and high accuracy to deal with complicated situations. Besides, to reduce the requirement of the large training dataset and annotation time of deep neural networks, we proposed the Random Background Transfer Method (RBTM) and Source Traceability Annotation Method (STAM) as the data augmentation and annotation method separately, which exploit the prior knowledge of the data and successfully realized the few-shot learning. Bases on the domain knowledge of dPCR images, our method could effectively augment images and reduce the labeling time by 70% while retaining the visually prominent features and improves the detection accuracy from 63.96% of the traditional threshold-based algorithm to as high as 98.98%. With the optimal processing speed and accuracy, our method is the state-of-art strategy for the detection of dPCR images now.
Highlights
With the global pandemic of the SARS-CoV-2-based disease (COVID-19), reverse transcription-polymerase chain reaction and real-time polymerase chain reaction, which adopt relative quantitative methods have exposed a serious shortcoming of the low accuracy
DATASET RESOURCE AND PREPARATION The droplet digital polymerase chain reaction (dPCR) images with uneven illumination are acquired from the previous work of Wu et al in Zhejiang University [23]
The average accuracy of the traditional threshold segmentation method is less than 65%, and the false positive rate is as high as over 40%, which makes it difficult to achieve effective detection
Summary
With the global pandemic of the SARS-CoV-2-based disease (COVID-19), reverse transcription-polymerase chain reaction and real-time polymerase chain reaction, which adopt relative quantitative methods have exposed a serious shortcoming of the low accuracy. The demand for accurate disease detection is daily increasing [1], [2]. Digital polymerase chain reaction (dPCR) adopts an absolute quantitative method and owns high accuracy which far beyond the relative quantitative polymerase chain reaction. According to the study of Valeria Cento, for patients who had false-negative results of the reverse transcription-polymerase chain reaction but with clinical symptoms, the detection results of the droplet dPCR which was verified by antibody detection have the accuracy of 100% and 95% for positive and negative results separately [3]. DPCR is playing an increasingly important role in the detection of pathogens.
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