Search Results for author:
Found 8 papers, 2 papers with code
Tool Calling: Enhancing Medication Consultation via Retrieval-Augmented Large Language Models
With experimental results, we show that our framework brings notable performance improvements and surpasses the previous counterparts in the evidence retrieval process in terms of evidence retrieval accuracy.
Grounded Knowledge-Enhanced Medical VLP for Chest X-Ray
Medical vision-language pre-training has emerged as a promising approach for learning domain-general representations of medical image and text.
GuideGen: A Text-guided Framework for Joint CT Volume and Anatomical structure Generation
Secondly, our Conditional Image Generator autoregressively generates CT slices conditioned on a corresponding mask slice to incorporate both style information and anatomical guidance.
Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation
Combining images from multi-modalities is beneficial to explore various information in computer vision, especially in the medical domain.
ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting
The long-tailed distribution problem in medical image analysis reflects a high prevalence of common conditions and a low prevalence of rare ones, which poses a significant challenge in developing a unified model capable of identifying rare or novel tumor categories not encountered during training.
KiUT: Knowledge-injected U-Transformer for Radiology Report Generation
Radiology report generation aims to automatically generate a clinically accurate and coherent paragraph from the X-ray image, which could relieve radiologists from the heavy burden of report writing.
One for All: One-stage Referring Expression Comprehension with Dynamic Reasoning
However, one unsolved issue of these models is that the number of reasoning steps needs to be pre-defined and fixed before inference, ignoring the varying complexity of expressions.
Attract me to Buy: Advertisement Copywriting Generation with Multimodal Multi-structured Information
Therefore, we present a dataset, E-MMAD (e-commercial multimodal multi-structured advertisement copywriting), which requires, and supports much more detailed information in text generation.
