31 citations · 64 across the 20 of their papers we have counts for
21 papers
SIRA: Reasoning-Aware Surgical Instrument Segmentation via Query-Anchored Alignment
Zhibo Zhang, Qijie Wang, Zengqiang Yan
Surgical instrument segmentation (SIS) plays a critical role in robotic assistance and surgical workflow analysis. However, most existing SIS methods formulate segmentation as a ca…
FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis
Nannan Wu, Zengqiang Yan, Nong Sang +2
Training a model that effectively handles both common and rare data-i.e., achieving performance fairness-is crucial in federated learning (FL). While existing fair FL methods have…
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching
Nannan Wu, Zhuo Kuang, Zengqiang Yan +2
Despite the potential of federated learning in medical applications, inconsistent imaging quality across institutions-stemming from lower-quality data from a minority of clients-bi…
PASSION: Towards Effective Incomplete Multi-Modal Medical Image Segmentation with Imbalanced Missing Rates
Junjie Shi, Caozhi Shang, Zhaobin Sun +3
Incomplete multi-modal image segmentation is a fundamental task in medical imaging to refine deployment efficiency when only partial modalities are available. However, the common p…
Non-parametric regularization for class imbalance federated medical image classification
Jeffry Wicaksana, Zengqiang Yan, Kwang-Ting Cheng
Limited training data and severe class imbalance pose significant challenges to developing clinically robust deep learning models. Federated learning (FL) addresses the former by e…
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completeness
Yangyang Xiang, Nannan Wu, Li Yu +3
Federated learning has emerged as a compelling paradigm for medical image segmentation, particularly in light of increasing privacy concerns. However, most of the existing research…
FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity
Zhaobin Sun, Nannan Wu, Junjie Shi +4
Cross-silo federated learning (FL) enables decentralized organizations to collaboratively train models while preserving data privacy and has made significant progress in medical im…
From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching
Nannan Wu, Zhuo Kuang, Zengqiang Yan +1
Due to escalating privacy concerns, federated learning has been recognized as a vital approach for training deep neural networks with decentralized medical data. In practice, it is…
SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts
Xian Lin, Yangyang Xiang, Zhehao Wang +3
Segment anything model (SAM), a foundation model with superior versatility and generalization across diverse segmentation tasks, has attracted widespread attention in medical imagi…
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise
Nannan Wu, Zhaobin Sun, Zengqiang Yan +1
Federated learning (FL) has emerged as a promising paradigm for training segmentation models on decentralized medical data, owing to its privacy-preserving property. However, exist…
ConvFormer: Plug-and-Play CNN-Style Transformers for Improving Medical Image Segmentation
Xian Lin, Zengqiang Yan, Xianbo Deng +2
Transformers have been extensively studied in medical image segmentation to build pairwise long-range dependence. Yet, relatively limited well-annotated medical image data makes tr…
Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting
Xian Lin, Yangyang Xiang, Li Yu +1
End-to-end medical image segmentation is of great value for computer-aided diagnosis dominated by task-specific models, usually suffering from poor generalization. With recent brea…
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity
Nannan Wu, Li Yu, Xuefeng Jiang +2
Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning. Existing research, relying on the assumption of cl…
FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring
Jeffry Wicaksana, Zengqiang Yan, Kwang-Ting Cheng
Limited training data and severe class imbalance impose significant challenges to developing clinically robust deep learning models. Federated learning (FL) addresses the former by…
Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation
Tianyi Shi, Xiaohuan Ding, Wei Zhou +4
Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. However, achieving high pixel-w…
The Lighter The Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning
Xian Lin, Li Yu, Kwang-Ting Cheng +1
Vision transformers have recently set off a new wave in the field of medical image analysis due to their remarkable performance on various computer vision tasks. However, recent hy…
BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation
Xian Lin, Li Yu, Kwang-Ting Cheng +1
Objective: Transformers, born to remedy the inadequate receptive fields of CNNs, have drawn explosive attention recently. However, the daunting computational complexity of global r…
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification
Nannan Wu, Li Yu, Xin Yang +2
Federated learning (FL), training deep models from decentralized data without privacy leakage, has shown great potential in medical image computing recently. However, considering t…
FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation
Jeffry Wicaksana, Zengqiang Yan, Dong Zhang +4
The purpose of federated learning is to enable multiple clients to jointly train a machine learning model without sharing data. However, the existing methods for training an image…
Exploring intermediate representation for monocular vehicle pose estimation
Shichao Li, Zengqiang Yan, Hongyang Li +1
We present a new learning-based framework to recover vehicle pose in SO(3) from a single RGB image. In contrast to previous works that map from local appearance to observation angl…
Describing upper body motions based on the Labanotation for learning-from-observation robots
Katsushi Ikeuchi, Zengqiang Yan, Zhaoyuan Ma +3
We have been developing a paradigm, which we refer to as Learning-from-observation, for a robot to automatically acquire what-to-do through observation of human performance. Since…