Search Results for author:
Found 4 papers, 2 papers with code
FedDRO: Federated Compositional Optimization for Distributionally Robust Learning
A key novelty of our work is to develop solution accuracy-independent algorithms that do not require large batch gradients (and function evaluations) for solving federated CO problems.
GeoSAM: Fine-tuning SAM with Sparse and Dense Visual Prompting for Automated Segmentation of Mobility Infrastructure
The Segment Anything Model (SAM) has shown impressive performance when applied to natural image segmentation.
Auto-Prompting SAM for Mobile Friendly 3D Medical Image Segmentation
In addition to the domain gaps between natural and medical images, disparities in the spatial arrangement between 2D and 3D images, the substantial computational burden imposed by powerful GPU servers, and the time-consuming manual prompt generation impede the extension of SAM to a broader spectrum of medical image segmentation applications.
FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images
Computed Tomography (CT) based precise prostate segmentation for treatment planning is challenging due to (1) the unclear boundary of the prostate derived from CT's poor soft tissue contrast and (2) the limitation of convolutional neural network-based models in capturing long-range global context.
