3 citations · 10 across the 14 of their papers we have counts for
15 papers
Dual Semantic-Aware Network for Noise Suppressed Ultrasound Video Segmentation
Ling Zhou, Runtian Yuan, Yi Liu +3
Ultrasound imaging is a prevalent diagnostic tool known for its simplicity and non-invasiveness. However, its inherent characteristics often introduce substantial noise, posing con…
Advancing Lung Disease Diagnosis in 3D CT Scans
Qingqiu Li, Runtian Yuan, Junlin Hou +4
To enable more accurate diagnosis of lung disease in chest CT scans, we propose a straightforward yet effective model. Firstly, we analyze the characteristics of 3D CT scans and re…
AOR: Anatomical Ontology-Guided Reasoning for Medical Large Multimodal Model in Chest X-Ray Interpretation
Qingqiu Li, Zihang Cui, Seongsu Bae +8
Chest X-rays (CXRs) are the most frequently performed imaging examinations in clinical settings. Recent advancements in Large Multimodal Models (LMMs) have enabled automated CXR in…
An Empirical Analysis of Uncertainty in Large Language Model Evaluations
Qiujie Xie, Qingqiu Li, Zhuohao Yu +3
As LLM-as-a-Judge emerges as a new paradigm for assessing large language models (LLMs), concerns have been raised regarding the alignment, bias, and stability of LLM evaluators. Wh…
Text-Promptable Propagation for Referring Medical Image Sequence Segmentation
Runtian Yuan, Mohan Chen, Jilan Xu +6
Referring Medical Image Sequence Segmentation (Ref-MISS) is a novel and challenging task that aims to segment anatomical structures in medical image sequences (\emph{e.g.} endoscop…
FineMedLM-o1: Enhancing Medical Knowledge Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training
Hongzhou Yu, Tianhao Cheng, Yingwen Wang +6
Recent advancements in large language models (LLMs) have shown promise in medical applications such as disease diagnosis and treatment planning. However, most existing medical LLMs…
CT2C-QA: Multimodal Question Answering over Chinese Text, Table and Chart
Bowen Zhao, Tianhao Cheng, Yuejie Zhang +3
Multimodal Question Answering (MMQA) is crucial as it enables comprehensive understanding and accurate responses by integrating insights from diverse data representations such as t…
Advancing COVID-19 Detection in 3D CT Scans
Qingqiu Li, Runtian Yuan, Junlin Hou +4
To make a more accurate diagnosis of COVID-19, we propose a straightforward yet effective model. Firstly, we analyse the characteristics of 3D CT scans and remove the non-lung part…
Domain Adaptation Using Pseudo Labels for COVID-19 Detection
Runtian Yuan, Qingqiu Li, Junlin Hou +4
In response to the need for rapid and accurate COVID-19 diagnosis during the global pandemic, we present a two-stage framework that leverages pseudo labels for domain adaptation to…
Anatomical Structure-Guided Medical Vision-Language Pre-training
Qingqiu Li, Xiaohan Yan, Jilan Xu +6
Learning medical visual representations through vision-language pre-training has reached remarkable progress. Despite the promising performance, it still faces challenges, i.e., lo…
Human Simulacra: Benchmarking the Personification of Large Language Models
Qiuejie Xie, Qiming Feng, Tianqi Zhang +7
Large language models (LLMs) are recognized as systems that closely mimic aspects of human intelligence. This capability has attracted attention from the social science community,…
Large Language Models are Complex Table Parsers
Bowen Zhao, Changkai Ji, Yuejie Zhang +5
With the Generative Pre-trained Transformer 3.5 (GPT-3.5) exhibiting remarkable reasoning and comprehension abilities in Natural Language Processing (NLP), most Question Answering…
DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors
Xiaze Zhang, Ziheng Ding, Qi Jing +3
Point clouds have shown significant potential in various domains, including Simultaneous Localization and Mapping (SLAM). However, existing approaches either rely on dense point cl…
Open-Set Image Tagging with Multi-Grained Text Supervision
Xinyu Huang, Yi-Jie Huang, Youcai Zhang +6
In this paper, we introduce the Recognize Anything Plus Model (RAM++), an open-set image tagging model effectively leveraging multi-grained text supervision. Previous approaches (e…
Enhanced Knowledge Injection for Radiology Report Generation
Qingqiu Li, Jilan Xu, Runtian Yuan +5
Automatic generation of radiology reports holds crucial clinical value, as it can alleviate substantial workload on radiologists and remind less experienced ones of potential anoma…