Zobrazeno 1 - 10
of 3 158
pro vyhledávání: '"Xue, Chun"'
The multimodal model has demonstrated promise in histopathology. However, most multimodal models are based on H\&E and genomics, adopting increasingly complex yet black-box designs. In our paper, we propose a novel interpretable multimodal framework
Externí odkaz:
http://arxiv.org/abs/2410.01408
Deploying large language models (LLMs) on edge devices presents significant challenges due to the substantial computational overhead and memory requirements. Activation sparsification can mitigate these challenges by reducing the number of activated
Externí odkaz:
http://arxiv.org/abs/2409.01366
Whole slide images (WSIs) are gigapixel-scale digital images of H\&E-stained tissue samples widely used in pathology. The substantial size and complexity of WSIs pose unique analytical challenges. Multiple Instance Learning (MIL) has emerged as a pow
Externí odkaz:
http://arxiv.org/abs/2408.09476
Autor:
Wu, Shangyu, Xiong, Ying, Cui, Yufei, Wu, Haolun, Chen, Can, Yuan, Ye, Huang, Lianming, Liu, Xue, Kuo, Tei-Wei, Guan, Nan, Xue, Chun Jason
Large language models (LLMs) have demonstrated great success in various fields, benefiting from their huge amount of parameters that store knowledge. However, LLMs still suffer from several key issues, such as hallucination problems, knowledge update
Externí odkaz:
http://arxiv.org/abs/2407.13193
While large language models (LLMs) have been used for automated grading, they have not yet achieved the same level of performance as humans, especially when it comes to grading complex questions. Existing research on this topic focuses on a particula
Externí odkaz:
http://arxiv.org/abs/2405.19694
Autor:
Zhou, Zikang, Hu, Haibo, Chen, Xinhong, Wang, Jianping, Guan, Nan, Wu, Kui, Li, Yung-Hui, Huang, Yu-Kai, Xue, Chun Jason
Simulating realistic behaviors of traffic agents is pivotal for efficiently validating the safety of autonomous driving systems. Existing data-driven simulators primarily use an encoder-decoder architecture to encode the historical trajectories befor
Externí odkaz:
http://arxiv.org/abs/2405.17372
Autor:
Huang, Lianming, Wu, Shangyu, Cui, Yufei, Xiong, Ying, Liu, Xue, Kuo, Tei-Wei, Guan, Nan, Xue, Chun Jason
Deploying large language model inference remains challenging due to their high computational overhead. Early exiting optimizes model inference by adaptively reducing the number of inference layers. Existing methods typically train internal classifier
Externí odkaz:
http://arxiv.org/abs/2405.15198
Immunohistochemistry (IHC) plays a crucial role in pathology as it detects the over-expression of protein in tissue samples. However, there are still fewer machine learning model studies on IHC's impact on accurate cancer grading. We discovered that
Externí odkaz:
http://arxiv.org/abs/2405.08197
Pre-processing whole slide images (WSIs) can impact classification performance. Our study shows that using fixed hyper-parameters for pre-processing out-of-domain WSIs can significantly degrade performance. Therefore, it is critical to search domain-
Externí odkaz:
http://arxiv.org/abs/2404.11161
Deploying Large Language Models (LLMs) on edge or mobile devices offers significant benefits, such as enhanced data privacy and real-time processing capabilities. However, it also faces critical challenges due to the substantial memory requirement of
Externí odkaz:
http://arxiv.org/abs/2403.01384