4 citations · 5 across the 5 of their papers we have counts for
5 papers
From Cold Start to Active Learning: Embedding-Based Scan Selection for Medical Image Segmentation
Devon Levy, Bar Assayag, Laura Gaspar +2
Accurate segmentation annotations are critical for disease monitoring, yet manual labeling remains a major bottleneck due to the time and expertise required. Active learning (AL) a…
SingleStrip: learning skull-stripping from a single labeled example
Bella Specktor-Fadida, Malte Hoffmann
Deep learning segmentation relies heavily on labeled data, but manual labeling is laborious and time-consuming, especially for volumetric images such as brain magnetic resonance im…
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp +67
Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI an…
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…