activity
20232026
most citedA versatile foundation model for cine cardiac magnetic resonance image analysis tasks

1 citations · 3 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

Causal-Adversarial Probing of Clinical Covariates for Prostate MRI Grading

Yipei Wang, Shiqi Huang, Wen Yan +6

Deep learning models for prostate MRI-based cancer grading may encode clinical covariates that either reflect useful disease-related signal or non-generalising shortcut information…

cs.CV2026

Maximizing T2-Only Prostate Cancer Localization from Expected Diffusion Weighted Imaging

Weixi Yi, Yipei Wang, Wen Yan +10

Multiparametric MRI is increasingly recommended as a first-line noninvasive approach to detect and localize prostate cancer, requiring at minimum diffusion-weighted (DWI) and T2-we…

cs.CV2026

ProFound: A moderate-sized vision foundation model for multi-task prostate imaging

Yipei Wang, Yinsong Xu, Weixi Yi +11

Many diagnostic and therapeutic clinical tasks for prostate cancer increasingly rely on multi-parametric MRI. Automating these tasks is challenging because they necessitate expert…

cs.CV2026★ 1 cited

Depth Augmented and FE Free 3D/2D Liver Registration for Laparoscopic Liver AR

Hanyuan Zhang, Lucas He, Runlong He +6

Augmented reality (AR) guidance in laparoscopic liver surgery requires accurate registration of preoperative 3D models to intraoperative 2D video, but remains challenging due to pa…

cs.CV2025

Analysis of Image-and-Text Uncertainty Propagation in Multimodal Large Language Models with Cardiac MR-Based Applications

Yucheng Tang, Yunguan Fu, Weixi Yi +4

Multimodal large language models (MLLMs) can process and integrate information from multimodality sources, such as text and images. However, interrelationship among input modalitie…

cs.CV2023

Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images

Weixi Yi, Vasilis Stavrinides, Zachary M. C. Baum +5

We propose Boundary-RL, a novel weakly supervised segmentation method that utilises only patch-level labels for training. We envision the segmentation as a boundary detection probl…