5 papers
CFCML: A Coarse-to-Fine Crossmodal Learning Framework For Disease Diagnosis Using Multimodal Images and Tabular Data
Tianling Liu, Hongying Liu, Fanhua Shang +3
In clinical practice, crossmodal information including medical images and tabular data is essential for disease diagnosis. There exists a significant modality gap between these dat…
iLLaVA: An Image is Worth Fewer Than 1/3 Input Tokens in Large Multimodal Models
Lianyu Hu, Liqing Gao, Fanhua Shang +2
Recent methods have made notable progress in accelerating Large Vision-Language Models (LVLMs) by exploiting the inherent redundancy in visual inputs. Most existing approaches, how…
PDD: Manifold-Prior Diverse Distillation for Medical Anomaly Detection
Xijun Lu, Hongying Liu, Fanhua Shang +2
Medical image anomaly detection faces unique challenges due to subtle, heterogeneous anomalies embedded in complex anatomical structures. Through systematic Grad-CAM analysis, we r…
LightVLM: Acceleraing Large Multimodal Models with Pyramid Token Merging and KV Cache Compression
Lianyu Hu, Fanhua Shang, Wei Feng +1
In this paper, we introduce LightVLM, a simple but effective method that can be seamlessly deployed upon existing Vision-Language Models (VLMs) to greatly accelerate the inference…
Completed Feature Disentanglement Learning for Multimodal MRIs Analysis
Tianling Liu, Hongying Liu, Fanhua Shang +3
Multimodal MRIs play a crucial role in clinical diagnosis and treatment. Feature disentanglement (FD)-based methods, aiming at learning superior feature representations for multimo…