6 citations · 13 across the 11 of their papers we have counts for
11 papers
Towards a Multimodal Large Language Model with Pixel-Level Insight for Biomedicine
Xiaoshuang Huang, Lingdong Shen, Jia Liu +4
In recent years, Multimodal Large Language Models (MLLM) have achieved notable advancements, demonstrating the feasibility of developing an intelligent biomedical assistant. Howeve…
A Refer-and-Ground Multimodal Large Language Model for Biomedicine
Xiaoshuang Huang, Haifeng Huang, Lingdong Shen +4
With the rapid development of multimodal large language models (MLLMs), especially their capabilities in visual chat through refer and ground functionalities, their significance is…
SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging
Lingdong Shen, Fangxin Shang, Xiaoshuang Huang +3
In the field of medical image segmentation, tackling Out-of-Distribution (OOD) segmentation tasks in a cost-effective manner remains a significant challenge. Universal segmentation…
SynFundus-1M: A High-quality Million-scale Synthetic fundus images Dataset with Fifteen Types of Annotation
Fangxin Shang, Jie Fu, Yehui Yang +3
Large-scale public datasets with high-quality annotations are rarely available for intelligent medical imaging research, due to data privacy concerns and the cost of annotations. I…
SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer
Junde Wu, Huihui Fang, Fangxin Shang +5
Clinically, the accurate annotation of lesions/tissues can significantly facilitate the disease diagnosis. For example, the segmentation of optic disc/cup (OD/OC) on fundus image w…
Learning self-calibrated optic disc and cup segmentation from multi-rater annotations
Junde Wu, Huihui Fang, Fangxin Shang +5
The segmentation of optic disc(OD) and optic cup(OC) from fundus images is an important fundamental task for glaucoma diagnosis. In the clinical practice, it is often necessary to…
One Hyper-Initializer for All Network Architectures in Medical Image Analysis
Fangxin Shang, Yehui Yang, Dalu Yang +3
Pre-training is essential to deep learning model performance, especially in medical image analysis tasks where limited training data are available. However, existing pre-training m…
An Effective Transformer-based Solution for RSNA Intracranial Hemorrhage Detection Competition
Fangxin Shang, Siqi Wang, Xiaorong Wang +1
We present an effective method for Intracranial Hemorrhage Detection (IHD) which exceeds the performance of the winner solution in RSNA-IHD competition (2019). Meanwhile, our model…
Opinions Vary? Diagnosis First!
Junde Wu, Huihui Fang, Dalu Yang +5
With the advancement of deep learning techniques, an increasing number of methods have been proposed for optic disc and cup (OD/OC) segmentation from the fundus images. Clinically,…
Robust Collaborative Learning of Patch-level and Image-level Annotations for Diabetic Retinopathy Grading from Fundus Image
Yehui Yang, Fangxin Shang, Binghong Wu +5
Diabetic retinopathy (DR) grading from fundus images has attracted increasing interest in both academic and industrial communities. Most convolutional neural network (CNN) based al…
Alternating Synthetic and Real Gradients for Neural Language Modeling
Fangxin Shang, Hao Zhang
Training recurrent neural networks (RNNs) with backpropagation through time (BPTT) has known drawbacks such as being difficult to capture longterm dependencies in sequences. Succes…