ABSTRACT
In order to establish the correct protocol for COVID-19 treatment, estimating the percentage of COVID-19 specific infection within the lung tissue can be an important tool. This article describes the approach we used in order to estimate the COVID-19 infection percentage on lung CT scan slices within the Covid-19-Infection-Percentage-Estimation-Challenge. Our method frames the regression problem as a multi-tasking process and is based on modern training pipelines and architectures that correspond to state of the art models on image classification tasks. It obtained the best score on the validation dataset and ranked third in the testing phase within the competition. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The paper presents a comparative analysis of several distinct approaches based on deep learning for identifying COVID-19 cases in chest CTs. A first approach is a volumetric one, involving 3D convolutions, while other two approaches perform at first slice-wise classification and then aggregate the results at the volume level. The experiments are carried on the COV19-CT-DB dataset, with the aim of addressing the challenge raised by the MIA-COV19D Competition within ICCV 2021. Our best results reach a macro F1 score of 92:34% on the validation subset and 90.06% on the test set, obtained with the volumetric approach which was ranked second in the competition. Its performance can be further improved by a simple trick, using semi-supervised training in the form of self-training, technique which proved to bring a consistent increase over the reported F1-score on the validation subset.