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cs.CV2023
Test-time augmentation-based active learning and self-training for label-efficient segmentation
Bella Specktor-Fadida, Anna Levchakov, Dana Schonberger +3
Deep learning techniques depend on large datasets whose annotation is time-consuming. To reduce annotation burden, the self-training (ST) and active-learning (AL) methods have been…
eess.IV2023
Simultaneous column-based deep learning progression analysis of atrophy associated with AMD in longitudinal OCT studies
Adi Szeskin, Roei Yehuda, Or Shmueli +2
Purpose: Disease progression of retinal atrophy associated with AMD requires the accurate quantification of the retinal atrophy changes on longitudinal OCT studies. It is based on…