1.2k citations · 1.4k across the 10 of their papers we have counts for
10 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…
Contour Dice loss for structures with Fuzzy and Complex Boundaries in Fetal MRI
Bella Specktor Fadida, Bossmat Yehuda, Daphna Link Sourani +3
Volumetric measurements of fetal structures in MRI are time consuming and error prone and therefore require automatic segmentation. Placenta segmentation and accurate fetal brain s…
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…
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…
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…
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Kelly Payette, Hongwei Li, Priscille de Dumast +55
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital ste…
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…
Automatic detection and diagnosis of sacroiliitis in CT scans as incidental findings
Yigal Shenkman, Bilal Qutteineh, Leo Joskowicz +4
Early diagnosis of sacroiliitis may lead to preventive treatment which can significantly improve the patient's quality of life in the long run. Oftentimes, a CT scan of the lower b…
The Liver Tumor Segmentation Benchmark (LiTS)
Patrick Bilic, Patrick Christ, Hongwei Bran Li +106
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedi…