1 citations · 2 across the 3 of their papers we have counts for
3 papers
eess.IV2024
Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities
Yuli Wang, Victoria Shi, Wen-Chi Hsu +9
Purpose: To evaluate various Segmental Anything Model (SAM) prompt strategies across four lesions datasets and to subsequently develop a reinforcement learning (RL) agent to optimi…
cs.LG2023★ 1 cited
Evidential Uncertainty Quantification: A Variance-Based Perspective
Ruxiao Duan, Brian Caffo, Harrison X. Bai +2
Uncertainty quantification of deep neural networks has become an active field of research and plays a crucial role in various downstream tasks such as active learning. Recent advan…
cs.CV2023★ 1 cited
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization
Daniel D Kim, Rajat S Chandra, Jian Peng +14
Deep learning models have demonstrated great potential in medical 3D imaging, but their development is limited by the expensive, large volume of annotated data required. Active lea…