32 citations · 37 across the 4 of their papers we have counts for
4 papers
SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images
Bella Specktor-Fadida, Liat Ben-Sira, Dafna Ben-Bashat +1
Quality control of structures segmentation in volumetric medical images is important for identifying segmentation errors in clinical practice and for facilitating model development…
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…
Automatic fetal fat quantification from MRI
Netanell Avisdris, Aviad Rabinowich, Daniel Fridkin +9
Normal fetal adipose tissue (AT) development is essential for perinatal well-being. AT, or simply fat, stores energy in the form of lipids. Malnourishment may result in excessive o…
Automatic linear measurements of the fetal brain on MRI with deep neural networks
Netanell Avisdris, Bossmat Yehuda, Ori Ben-Zvi +6
Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal b…