3 citations · 3 across the 4 of their papers we have counts for
4 papers
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…
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…
Partial annotations for the segmentation of large structures with low annotation cost
Bella Specktor Fadida, Daphna Link Sourani, Liat Ben Sira Elka Miller +2
Deep learning methods have been shown to be effective for the automatic segmentation of structures and pathologies in medical imaging. However, they require large annotated dataset…
BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planes
Netanell Avisdris, Leo Joskowicz, Brian Dromey +5
Fetal growth assessment from ultrasound is based on a few biometric measurements that are performed manually and assessed relative to the expected gestational age. Reliable biometr…