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COVID Detection in Chest CTs: Improving the Baseline on COV19-CT-DB

Radu Miron, Cosmin Moisii, Sergiu Dinu, Mihaela Breaban

TL;DR

A comparative analysis of three distinct approaches based on deep learning for COVID-19 detection in chest CTs, involving 3D convolutions, is presented, with the aim of addressing the challenge raised by the MIA-COV19D Competition within ICCV 2021.

Abstract

The paper presents a comparative analysis of three distinct approaches based on deep learning for COVID-19 detection in chest CTs. The first approach is a volumetric one, involving 3D convolutions, while the 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 on the validation subset reach a macro-F1 score of 0.92, which improves considerably the baseline score of 0.70 set by the organizers.

COVID Detection in Chest CTs: Improving the Baseline on COV19-CT-DB

TL;DR

A comparative analysis of three distinct approaches based on deep learning for COVID-19 detection in chest CTs, involving 3D convolutions, is presented, with the aim of addressing the challenge raised by the MIA-COV19D Competition within ICCV 2021.

Abstract

The paper presents a comparative analysis of three distinct approaches based on deep learning for COVID-19 detection in chest CTs. The first approach is a volumetric one, involving 3D convolutions, while the 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 on the validation subset reach a macro-F1 score of 0.92, which improves considerably the baseline score of 0.70 set by the organizers.

Paper Structure

This paper contains 7 sections, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. The dataset
  3. A volumetric approach
  4. A slice based approach with mini-volumes
  5. A slice based approach tackled with neural network architecture search
  6. Experimental results
  7. Conclusions

Figures (2)

  • Figure 1: The primitive found by the sharpDARTS search algorithm.
  • Figure 2: The final architecture found by the sharpDARTS search algorithm.