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Associated SPS Event: IEEE ICASSP 2021 Grand Challenge
Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries around the world affecting millions and claiming more than 1.5 million human lives, since its first emergence in late 2019. This highly contagious disease can easily spread, and if not controlled in a timely fashion, can rapidly incapacitate healthcare systems.
The main objective of the 2021 IEEE SPGC-COVID is development of fully automated frameworks to identify/classify COVID-19 infections using only volumetric chest CT scans. The introduced SPGC-COVID dataset is a large dataset of COVID-19, CAP, and normal cases acquired with various imaging settings from different medical centers. The challenge is to design advanced and robust learning models to classify the given CT scans into three classes of COVID-19, CAP, and normal cases. Developed learning models need to perform accurately and robustly over such heterogeneous set of CT scans, which include images with different slice thickness, radiation dose, and noise level. In addition to acquisition and visual variations of CT scans, the SPGC-COVID dataset consists of CT scans that, beside COVID-19 infections, include manifestations related to hearth problems/operations.
Any team can participate in the competition, should complete their submission by March 1st, 2021. The five best teams are selected and announced by May 25th, 2021. Three finalist teams will be judged at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2021, which will be held June 6-12, 2021, Toronto, Canada. In addition to algorithmic performances, demonstration and presentation performances will also affect the final ranking.
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