Zobrazeno 1 - 10
of 1 377
pro vyhledávání: '"ZHU, HONGTU"'
Autor:
Sun, Yongheng, Lee, Yueh Z., Woodard, Genevieve A., Zhu, Hongtu, Lian, Chunfeng, Liu, Mingxia
Radiology report generation is crucial in medical imaging,but the manual annotation process by physicians is time-consuming and labor-intensive, necessitating the develop-ment of automatic report generation methods. Existingresearch predominantly uti
Externí odkaz:
http://arxiv.org/abs/2410.18135
Autor:
Tsai, Ting Yu, Lin, Li, Hu, Shu, Tsao, Connie W., Li, Xin, Chang, Ming-Ching, Zhu, Hongtu, Wang, Xin
Building on the success of deep learning models in cardiovascular structure segmentation, increasing attention has been focused on improving generalization and robustness, particularly in small, annotated datasets. Despite recent advancements, curren
Externí odkaz:
http://arxiv.org/abs/2409.14305
Time series experiments, in which experimental units receive a sequence of treatments over time, are frequently employed in many technological companies to evaluate the performance of a newly developed policy, product, or treatment relative to a base
Externí odkaz:
http://arxiv.org/abs/2408.05342
Medical Image Foundation Models have proven to be powerful tools for mask prediction across various datasets. However, accurately assessing the uncertainty of their predictions remains a significant challenge. To address this, we propose a new model,
Externí odkaz:
http://arxiv.org/abs/2408.08881
Autor:
Dai, Runpeng, Wang, Jianing, Zhou, Fan, Luo, Shikai, Qin, Zhiwei, Shi, Chengchun, Zhu, Hongtu
Off-policy evaluation (OPE) is widely applied in sectors such as pharmaceuticals and e-commerce to evaluate the efficacy of novel products or policies from offline datasets. This paper introduces a causal deepset framework that relaxes several key st
Externí odkaz:
http://arxiv.org/abs/2407.17910
Autor:
Xia, Peng, Zhu, Kangyu, Li, Haoran, Zhu, Hongtu, Li, Yun, Li, Gang, Zhang, Linjun, Yao, Huaxiu
The recent emergence of Medical Large Vision Language Models (Med-LVLMs) has enhanced medical diagnosis. However, current Med-LVLMs frequently encounter factual issues, often generating responses that do not align with established medical facts. Retr
Externí odkaz:
http://arxiv.org/abs/2407.05131
We propose Nodewise Loreg, a nodewise $L_0$-penalized regression method for estimating high-dimensional sparse precision matrices. We establish its asymptotic properties, including convergence rates, support recovery, and asymptotic normality under h
Externí odkaz:
http://arxiv.org/abs/2406.06481
Autor:
Xia, Peng, Chen, Ze, Tian, Juanxi, Gong, Yangrui, Hou, Ruibo, Xu, Yue, Wu, Zhenbang, Fan, Zhiyuan, Zhou, Yiyang, Zhu, Kangyu, Zheng, Wenhao, Wang, Zhaoyang, Wang, Xiao, Zhang, Xuchao, Bansal, Chetan, Niethammer, Marc, Huang, Junzhou, Zhu, Hongtu, Li, Yun, Sun, Jimeng, Ge, Zongyuan, Li, Gang, Zou, James, Yao, Huaxiu
Artificial intelligence has significantly impacted medical applications, particularly with the advent of Medical Large Vision Language Models (Med-LVLMs), sparking optimism for the future of automated and personalized healthcare. However, the trustwo
Externí odkaz:
http://arxiv.org/abs/2406.06007
This paper studies policy evaluation with multiple data sources, especially in scenarios that involve one experimental dataset with two arms, complemented by a historical dataset generated under a single control arm. We propose novel data integration
Externí odkaz:
http://arxiv.org/abs/2406.00317
Biomedical image segmentation is critical for accurate identification and analysis of anatomical structures in medical imaging, particularly in cardiac MRI. Manual segmentation is labor-intensive, time-consuming, and prone to errors, highlighting the
Externí odkaz:
http://arxiv.org/abs/2405.17496