Search Results for author: Kenneth K. Y. Wong

Found 5 papers, 3 papers with code

Diff4MMLiTS: Advanced Multimodal Liver Tumor Segmentation via Diffusion-Based Image Synthesis and Alignment

no code implementations29 Dec 2024 Shiyun Chen, Li Lin, Pujin Cheng, Zhicheng Jin, JianJian Chen, Haidong Zhu, Kenneth K. Y. Wong, Xiaoying Tang

Multimodal learning has been demonstrated to enhance performance across various clinical tasks, owing to the diverse perspectives offered by different modalities of data.

Image Generation Segmentation +1

FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation

1 code implementation27 Feb 2024 Li Lin, Yixiang Liu, Jiewei Wu, Pujin Cheng, Zhiyuan Cai, Kenneth K. Y. Wong, Xiaoying Tang

In such context, we propose a novel personalized FL framework with learnable prompt and aggregation (FedLPPA) to uniformly leverage heterogeneous weak supervision for medical image segmentation.

Decoder Federated Learning +5

Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation

1 code implementation12 Apr 2023 Li Lin, Jiewei Wu, Yixiang Liu, Kenneth K. Y. Wong, Xiaoying Tang

The statistical heterogeneity (e. g., non-IID data and domain shifts) is a primary obstacle in FL, impairing the generalization performance of the global model.

channel selection Federated Learning +5

GlocalFuse-Depth: Fusing Transformers and CNNs for All-day Self-supervised Monocular Depth Estimation

no code implementations20 Feb 2023 Zezheng Zhang, Ryan K. Y. Chan, Kenneth K. Y. Wong

In recent years, self-supervised monocular depth estimation has drawn much attention since it frees of depth annotations and achieved remarkable results on standard benchmarks.

All Monocular Depth Estimation

YoloCurvSeg: You Only Label One Noisy Skeleton for Vessel-style Curvilinear Structure Segmentation

1 code implementation11 Dec 2022 Li Lin, Linkai Peng, Huaqing He, Pujin Cheng, Jiewei Wu, Kenneth K. Y. Wong, Xiaoying Tang

With only one noisy skeleton annotation (respectively 0. 14\%, 0. 03\%, 1. 40\%, and 0. 65\% of the full annotation), YoloCurvSeg achieves more than 97\% of the fully-supervised performance on each dataset.

Contrastive Learning Image Generation +4

Cannot find the paper you are looking for? You can Submit a new open access paper.