24 citations · 91 across the 37 of their papers we have counts for
12 papers · 1 filter
Multimodal, Multi-Disease Medical Imaging Foundation Model (MerMED-FM)
Yang Zhou, Chrystie Wan Ning Quek, Jun Zhou +22
Current artificial intelligence models for medical imaging are predominantly single modality and single disease. Attempts to create multimodal and multi-disease models have resulte…
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…
From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning
Yuncheng Jiang, Chun-Mei Feng, Jinke Ren +15
Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, traditional ultrasound diagnostics relies heavily on phy…
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…
Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices
Hongye Zeng, Ke Zou, Zhihao Chen +10
Handheld ultrasound devices face usage limitations due to user inexperience and cannot benefit from supervised deep learning without extensive expert annotations. Moreover, the mod…
Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction
Yunlu Yan, Chun-Mei Feng, Yuexiang Li +2
While multi-modal learning has been widely used for MRI reconstruction, it relies on paired multi-modal data which is difficult to acquire in real clinical scenarios. Especially in…
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…
Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty
Ke Zou, Yidi Chen, Ling Huang +6
Medical image segmentation is critical for disease diagnosis and treatment assessment. However, concerns regarding the reliability of segmentation regions persist among clinicians,…
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…
Medical Image Segmentation Using Squeeze-and-Expansion Transformers
Shaohua Li, Xiuchao Sui, Xiangde Luo +3
Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn ima…
Learning to Reconstruct Crack Profiles for Eddy Current Nondestructive Testing
Shaohua Li, Ayesha Anees, Yu Zhong +4
Eddy current testing (ECT) is one of the most popular Nondestructive Testing (NDT) techniques, especially for conductive materials. Reconstructing the crack profile from measured E…