173 citations · 256 across the 28 of their papers we have counts for
29 papers
Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning
Chun-Mei Feng, Kai Yu, Xinxing Xu +4
Benefited from image-text contrastive learning, pre-trained vision-language models, e.g., CLIP, allow to direct leverage texts as images (TaI) for parameter-efficient fine-tuning (…
An integrated language-vision foundation model for conversational diagnostics and triaging in primary eye care
Zhi Da Soh, Yang Bai, Kai Yu +28
Current deep learning models are mostly task specific and lack a user-friendly interface to operate. We present Meta-EyeFM, a multi-function foundation model that integrates a larg…
Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation
Lei Zhu, Yanyu Xu, Huazhu Fu +3
Unpaired Multi-Modal Learning (UMML) which leverages unpaired multi-modal data to boost model performance on each individual modality has attracted a lot of research interests in m…
BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays
Yang Zhou, Tan Li Hui Faith, Yanyu Xu +4
Medical Vision-Language Pretraining (MedVLP) shows promise in learning generalizable and transferable visual representations from paired and unpaired medical images and reports. Me…
Enhancing Community Vision Screening -- AI Driven Retinal Photography for Early Disease Detection and Patient Trust
Xiaofeng Lei, Yih-Chung Tham, Jocelyn Hui Lin Goh +7
Community vision screening plays a crucial role in identifying individuals with vision loss and preventing avoidable blindness, particularly in rural communities where access to ey…
A New Perspective to Boost Performance Fairness for Medical Federated Learning
Yunlu Yan, Lei Zhu, Yuexiang Li +5
Improving the fairness of federated learning (FL) benefits healthy and sustainable collaboration, especially for medical applications. However, existing fair FL methods ignore the…
UrFound: Towards Universal Retinal Foundation Models via Knowledge-Guided Masked Modeling
Kai Yu, Yang Zhou, Yang Bai +5
Retinal foundation models aim to learn generalizable representations from diverse retinal images, facilitating label-efficient model adaptation across various ophthalmic tasks. Des…
CPT: Consistent Proxy Tuning for Black-box Optimization
Yuanyang He, Zitong Huang, Xinxing Xu +5
Black-box tuning has attracted recent attention due to that the structure or inner parameters of advanced proprietary models are not accessible. Proxy-tuning provides a test-time o…
Tailoring Generative Adversarial Networks for Smooth Airfoil Design
Joyjit Chattoraj, Jian Cheng Wong, Zhang Zexuan +8
In the realm of aerospace design, achieving smooth curves is paramount, particularly when crafting objects such as airfoils. Generative Adversarial Network (GAN), a widely employed…
Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax Classification
Han Yuan, Chuan Hong, Pengtao Jiang +5
Background: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. To address the opaqueness often associated with deep learn…
RLPeri: Accelerating Visual Perimetry Test with Reinforcement Learning and Convolutional Feature Extraction
Tanvi Verma, Linh Le Dinh, Nicholas Tan +3
Visual perimetry is an important eye examination that helps detect vision problems caused by ocular or neurological conditions. During the test, a patient's gaze is fixed at a spec…
A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design
Manna Dai, Yang Jiang, Feng Yang +6
Metasurfaces have widespread applications in fifth-generation (5G) microwave communication. Among the metasurface family, free-form metasurfaces excel in achieving intricate spectr…
Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning
Zitong Huang, Ze Chen, Zhixing Chen +6
Few-shot Class-Incremental Learning (FSCIL) aims to continuously learn new classes based on very limited training data without forgetting the old ones encountered. Existing studies…
VQA4CIR: Boosting Composed Image Retrieval with Visual Question Answering
Chun-Mei Feng, Yang Bai, Tao Luo +6
Albeit progress has been made in Composed Image Retrieval (CIR), we empirically find that a certain percentage of failure retrieval results are not consistent with their relative c…
Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation
Han Yuan, Chuan Hong, Nguyen Tuan Anh Tran +2
Pneumothorax is a medical emergency caused by abnormal accumulation of air in the pleural space - the potential space between the lungs and chest wall. On 2D chest radiographs, pne…
Sentence-level Prompts Benefit Composed Image Retrieval
Yang Bai, Xinxing Xu, Yong Liu +5
Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption. Most existing CIR models adop…
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification
Meng Wang, Tian Lin, Lianyu Wang +21
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classific…
Learning Federated Visual Prompt in Null Space for MRI Reconstruction
Chun-Mei Feng, Bangjun Li, Xinxing Xu +3
Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple hospitals to collaborate distributedly without aggregating local data, thereby protecting patient privacy…
Medical Phrase Grounding with Region-Phrase Context Contrastive Alignment
Zhihao Chen, Yang Zhou, Anh Tran +8
Medical phrase grounding (MPG) aims to locate the most relevant region in a medical image, given a phrase query describing certain medical findings, which is an important task for…
Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging
Meng Wang, Lianyu Wang, Xinxing Xu +5
Deep learning models have shown promising performance in the field of diabetic retinopathy (DR) staging. However, collaboratively training a DR staging model across multiple instit…
Reliable Federated Disentangling Network for Non-IID Domain Feature
Meng Wang, Kai Yu, Chun-Mei Feng +6
Federated learning (FL), as an effective decentralized distributed learning approach, enables multiple institutions to jointly train a model without sharing their local data. Howev…
Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images
Meng Wang, Kai Yu, Chun-Mei Feng +6
Focusing on the complicated pathological features, such as blurred boundaries, severe scale differences between symptoms, background noise interference, etc., in the task of retina…
Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks
Xiaofeng Lei, Shaohua Li, Xinxing Xu +9
Localizing anatomical landmarks are important tasks in medical image analysis. However, the landmarks to be localized often lack prominent visual features. Their locations are elus…
CRAFT: Cross-Attentional Flow Transformer for Robust Optical Flow
Xiuchao Sui, Shaohua Li, Xue Geng +5
Optical flow estimation aims to find the 2D motion field by identifying corresponding pixels between two images. Despite the tremendous progress of deep learning-based optical flow…