9 citations · 9 across the 3 of their papers we have counts for
5 papers
DEAP-3DSAM: Decoder Enhanced and Auto Prompt SAM for 3D Medical Image Segmentation
Fangda Chen, Jintao Tang, Pancheng Wang +3
The Segment Anything Model (SAM) has recently demonstrated significant potential in medical image segmentation. Although SAM is primarily trained on 2D images, attempts have been m…
Beyond Plain Demos: A Demo-centric Anchoring Paradigm for In-Context Learning in Alzheimer's Disease Detection
Puzhen Su, Haoran Yin, Yongzhu Miao +3
Detecting Alzheimer's disease (AD) from narrative transcripts challenges large language models (LLMs): pre-training rarely covers this out-of-distribution task, and all transcript…
Explicit Knowledge-Guided In-Context Learning for Early Detection of Alzheimer's Disease
Puzhen Su, Yongzhu Miao, Chunxi Guo +3
Detecting Alzheimer's Disease (AD) from narrative transcripts remains a challenging task for large language models (LLMs), particularly under out-of-distribution (OOD) and data-sca…
Precise Zero-Shot Pointwise Ranking with LLMs through Post-Aggregated Global Context Information
Kehan Long, Shasha Li, Chen Xu +2
Recent advancements have successfully harnessed the power of Large Language Models (LLMs) for zero-shot document ranking, exploring a variety of prompting strategies. Comparative a…
Multi-Document Scientific Summarization from a Knowledge Graph-Centric View
Pancheng Wang, Shasha Li, Kunyuan Pang +4
Multi-Document Scientific Summarization (MDSS) aims to produce coherent and concise summaries for clusters of topic-relevant scientific papers. This task requires precise understan…