14 citations · 17 across the 2 of their papers we have counts for
3 papers
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…
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.…
SHREC 2011: robust feature detection and description benchmark
E. Boyer, A. M. Bronstein, M. M. Bronstein +15
Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. SHREC'11 r…