5 citations · 8 across the 9 of their papers we have counts for
7 papers · 1 filter
PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts
Hao Li, Han Liu, Dewei Hu +2
In this paper, we present PRISM, a Promptable and Robust Interactive Segmentation Model, aiming for precise segmentation of 3D medical images. PRISM accepts various visual inputs,…
Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts
Hao Li, Han Liu, Dewei Hu +2
Interactive segmentation model leverages prompts from users to produce robust segmentation. This advancement is facilitated by prompt engineering, where interactive prompts serve a…
MAP: Domain Generalization via Meta-Learning on Anatomy-Consistent Pseudo-Modalities
Dewei Hu, Hao Li, Han Liu +3
Deep models suffer from limited generalization capability to unseen domains, which has severely hindered their clinical applicability. Specifically for the retinal vessel segmentat…
False Negative/Positive Control for SAM on Noisy Medical Images
Xing Yao, Han Liu, Dewei Hu +9
The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. It can use sparse manual prompts such as bounding boxes to generate pixe…
VesselMorph: Domain-Generalized Retinal Vessel Segmentation via Shape-Aware Representation
Dewei Hu, Hao Li, Han Liu +3
Due to the absence of a single standardized imaging protocol, domain shift between data acquired from different sites is an inherent property of medical images and has become a maj…
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation
Han Liu, Hao Li, Xing Yao +6
Medical image segmentation is a critical task in medical image analysis. In recent years, deep learning based approaches have shown exceptional performance when trained on a fully-…