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eess.IV2025
Unmasking Interstitial Lung Diseases: Leveraging Masked Autoencoders for Diagnosis
Ethan Dack, Lorenzo Brigato, Vasilis Dedousis +9
Masked autoencoders (MAEs) have emerged as a powerful approach for pre-training on unlabelled data, capable of learning robust and informative feature representations. This is part…
eess.IV2023
An Empirical Analysis for Zero-Shot Multi-Label Classification on COVID-19 CT Scans and Uncurated Reports
Ethan Dack, Lorenzo Brigato, Matthew McMurray +9
The pandemic resulted in vast repositories of unstructured data, including radiology reports, due to increased medical examinations. Previous research on automated diagnosis of COV…