5 citations · 7 across the 2 of their papers we have counts for
2 papers
eess.IV2024★ 2 cited
MedSegMamba: 3D CNN-Mamba Hybrid Architecture for Brain Segmentation
Aaron Cao, Zongyu Li, Jordan Jomsky +2
Widely used traditional pipelines for subcortical brain segmentation are often inefficient and slow, particularly when processing large datasets. Furthermore, deep learning models…
eess.IV2023★ 5 cited
Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation
Ziyi Huang, Hongshan Liu, Haofeng Zhang +7
Segment anything model (SAM) has emerged as the leading approach for zero-shot learning in segmentation tasks, offering the advantage of avoiding pixel-wise annotations. It is part…