activity
20242026
most citedDeep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Bi-parametric MRI Datasets

7 citations · 8 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV2026

IntraStyler: Intra-Domain Style Synthesis for Cross-Modality MRI Domain Adaptation

Han Liu, Yubo Fan, Hao Li +5

Segmentation of vestibular schwannoma and cochlea from T2 MRI is clinically important yet annotation-intensive. Domain adaptation (DA) has been widely adopted to bridge the gap bet…

cs.CV2025

Endo-SemiS: Towards Robust Semi-Supervised Image Segmentation for Endoscopic Video

Hao Li, Daiwei Lu, Xing Yao +2

In this paper, we present Endo-SemiS, a semi-supervised segmentation framework for providing reliable segmentation of endoscopic video frames with limited annotation. EndoSemiS use…

cs.CV20241 cited

AdaptDiff: Cross-Modality Domain Adaptation via Weak Conditional Semantic Diffusion for Retinal Vessel Segmentation

Dewei Hu, Hao Li, Han Liu +4

Deep learning has shown remarkable performance in medical image segmentation. However, despite its promise, deep learning has many challenges in practice due to its inability to ef…

eess.IV20247 cited

Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Bi-parametric MRI Datasets

Hao Li, Han Liu, Heinrich von Busch +16

Our hypothesis is that UDA using diffusion-weighted images, generated with a unified model, offers a promising and reliable strategy for enhancing the performance of supervised lea…

eess.IV2024

Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging

Jiacheng Wang, Hao Li, Dewei Hu +4

We propose a novel framework for retinal feature point alignment, designed for learning cross-modality features to enhance matching and registration across multi-modality retinal i…

cs.CV2024

Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images

Hao Li, Baris Oguz, Gabriel Arenas +6

Placenta volume measurement from 3D ultrasound images is critical for predicting pregnancy outcomes, and manual annotation is the gold standard. However, such manual annotation is…