7 papers
CoRE: Concept-Reasoning Expansion for Continual Brain Lesion Segmentation
Qianqian Chen, Anglin Liu, Jingyang Zhang +1
Accurate brain lesion segmentation in MRI is vital for effective clinical diagnosis and treatment planning. Due to high annotation costs and strict data privacy regulations, univer…
MSLAU-Net: A Hybrid CNN-Transformer Network for Medical Image Segmentation
Libin Lan, Yanxin Li, Xiaojuan Liu +4
Accurate medical image segmentation allows for the precise delineation of anatomical structures and pathological regions, which is essential for treatment planning, surgical naviga…
Verb Mirage: Unveiling and Assessing Verb Concept Hallucinations in Multimodal Large Language Models
Zehao Wang, Xinpeng Liu, Yudonglin Zhang +6
Multimodal Large Language Models (MLLMs) have garnered significant attention recently and demonstrate outstanding capabilities in various tasks such as OCR, VQA, captioning, $\text…
HyperVL: An Efficient and Dynamic Multimodal Large Language Model for Edge Devices
HyperAI Team, Yuchen Liu, Kaiyang Han +26
Current multimodal large lanauge models possess strong perceptual and reasoning capabilities, however high computational and memory requirements make them difficult to deploy direc…
DMAF-Net: An Effective Modality Rebalancing Framework for Incomplete Multi-Modal Medical Image Segmentation
Libin Lan, Hongxing Li, Zunhui Xia +1
Incomplete multi-modal medical image segmentation faces critical challenges from modality imbalance, including imbalanced modality missing rates and heterogeneous modality contribu…
Cross-Modal Clustering-Guided Negative Sampling for Self-Supervised Joint Learning from Medical Images and Reports
Libin Lan, Hongxing Li, Zunhui Xia +5
Learning medical visual representations directly from paired images and reports through multimodal self-supervised learning has emerged as a novel and efficient approach to digital…