FetSelect pairs a frozen vision foundation model with a hybrid multi-head design and BYOL pretraining on ultrasound data to select quality fetal frames, reporting mean AUROC 0.956 on expert-labeled test data.
Walker, Steven Hawken, and Adrian D.C
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.
citing papers explorer
-
FetSelect: Task-Specific Architectures and Self-Supervised Learning for Automated Fetal Ultrasound Frame Selection
FetSelect pairs a frozen vision foundation model with a hybrid multi-head design and BYOL pretraining on ultrasound data to select quality fetal frames, reporting mean AUROC 0.956 on expert-labeled test data.
-
APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms
Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms with unified dataset and deployment interfaces.