5 citations · 14 across the 14 of their papers we have counts for
11 papers · 1 filter
SAMora: Enhancing SAM through Hierarchical Self-Supervised Pre-Training for Medical Images
Shuhang Chen, Hangjie Yuan, Pengwei Liu +3
The Segment Anything Model (SAM) has demonstrated significant potential in medical image segmentation. Yet, its performance is limited when only a small amount of labeled data is a…
Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
Hanxue Gu, Yaqian Chen, Nicholas Konz +2
Foundation models, pre-trained on large image datasets and capable of capturing rich feature representations, have recently shown potential for zero-shot image registration. Howeve…
MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
Haoyu Dong, Yuwen Chen, Hanxue Gu +4
The widespread use of Magnetic Resonance Imaging (MRI) in combination with deep learning shows promise for many high-impact automated diagnostic and prognostic tools. However, trai…
BreastSegNet: Multi-label Segmentation of Breast MRI
Qihang Li, Jichen Yang, Yaqian Chen +4
Breast MRI provides high-resolution imaging critical for breast cancer screening and preoperative staging. However, existing segmentation methods for breast MRI remain limited in s…
GuidedMorph: Two-Stage Deformable Registration for Breast MRI
Yaqian Chen, Hanxue Gu, Haoyu Dong +5
Accurately registering breast MR images from different time points enables the alignment of anatomical structures and tracking of tumor progression, supporting more effective breas…
Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2
Yuwen Chen, Zafer Yildiz, Qihang Li +5
Manual annotation of volumetric medical images, such as magnetic resonance imaging (MRI) and computed tomography (CT), is a labor-intensive and time-consuming process. Recent advan…