5 citations · 11 across the 7 of their papers we have counts for
8 papers
FRISM: Fine-Grained Reasoning Injection via Subspace-Level Model Merging for Vision-Language Models
Chenyu Huang, Peng Ye, Xudong Tan +4
Efficiently enhancing the reasoning capabilities of Vision-Language Models (VLMs) by merging them with Large Reasoning Models (LRMs) has emerged as a promising direction. However,…
OralGPT-Omni: A Versatile Dental Multimodal Large Language Model
Jing Hao, Yuci Liang, Lizhuo Lin +12
Multimodal Large Language Models (MLLMs) have exhibited immense potential across numerous medical specialties; yet, dentistry remains underexplored, in part due to limited domain-s…
Context-Gated Cross-Modal Perception with Visual Mamba for PET-CT Lung Tumor Segmentation
Elena Mulero Ayllón, Linlin Shen, Pierangelo Veltri +4
Accurate lung tumor segmentation is vital for improving diagnosis and treatment planning, and effectively combining anatomical and functional information from PET and CT remains a…
Mammo-Clustering: Context Clustering based Multi-view Tri Level Information Fusion for Lesion Location and Classification in Mammography
Shilong Yang, Chulong Zhang, Xiaokun Liang +9
Breast cancer is a significant global health issue, and the diagnosis of breast cancer through imaging remains challenging. Mammography images are characterized by extremely high r…
Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging
Filippo Ruffini, Elena Mulero Ayllon, Linlin Shen +2
Despite the significant potential of Foundation Models (FMs) in medical imaging, their application to prognosis prediction remains challenging due to data scarcity, class imbalance…
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging
Elena Mulero Ayllón, Massimiliano Mantegna, Linlin Shen +3
Accurate lung tumor segmentation is crucial for improving diagnosis, treatment planning, and patient outcomes in oncology. However, the complexity of tumor morphology, size, and lo…
Any-to-Any Vision-Language Model for Multimodal X-ray Imaging and Radiological Report Generation
Daniele Molino, Francesco di Feola, Linlin Shen +2
Generative models have revolutionized Artificial Intelligence (AI), particularly in multimodal applications. However, adapting these models to the medical domain poses unique chall…
XGeM: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation
Daniele Molino, Francesco Di Feola, Eliodoro Faiella +5
The adoption of Artificial Intelligence in medical imaging holds great promise, yet it remains hindered by challenges such as data scarcity, privacy concerns, and the need for robu…