most citedComparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients

14 citations · 17 across the 2 of their papers we have counts for

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eess.IV2020★ 3 cited

Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging

Abel Díaz Berenguer, Hichem Sahli, Boris Joukovsky +37

Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised c…

eess.IV2020★ 14 cited

Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients

Sofie Tilborghs, Ine Dirks, Lucas Fidon +19

Recent research on COVID-19 suggests that CT imaging provides useful information to assess disease progression and assist diagnosis, in addition to help understanding the disease.…