99 citations · 122 across the 20 of their papers we have counts for
9 papers · 1 filter
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…
Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models
Hao Li, Han Liu, Dewei Hu +2
To address prevalent issues in medical imaging, such as data acquisition challenges and label availability, transfer learning from natural to medical image domains serves as a viab…
Novel OCT mosaicking pipeline with Feature- and Pixel-based registration
Jiacheng Wang, Hao Li, Dewei Hu +2
High-resolution Optical Coherence Tomography (OCT) images are crucial for ophthalmology studies but are limited by their relatively narrow field of view (FoV). Image mosaicking is…
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…
CATS v2: Hybrid encoders for robust medical segmentation
Hao Li, Han Liu, Dewei Hu +3
Convolutional Neural Networks (CNNs) have exhibited strong performance in medical image segmentation tasks by capturing high-level (local) information, such as edges and textures.…
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-…
COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training
Han Liu, Zhoubing Xu, Riqiang Gao +5
Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, med…
SSL^2: Self-Supervised Learning meets Semi-Supervised Learning: Multiple Sclerosis Segmentation in 7T-MRI from large-scale 3T-MRI
Jiacheng Wang, Hao Li, Han Liu +6
Automated segmentation of multiple sclerosis (MS) lesions from MRI scans is important to quantify disease progression. In recent years, convolutional neural networks (CNNs) have sh…