Zobrazeno 1 - 10
of 651
pro vyhledávání: '"HAERI, MOHAMMAD"'
Autor:
Gu, Hongyan, Yang, Chunxu, Magaki, Shino, Zarrin-Khameh, Neda, Lakis, Nelli S., Cobos, Inma, Khanlou, Negar, Zhang, Xinhai R., Assi, Jasmeet, Byers, Joshua T., Hamza, Ameer, Han, Karam, Meyer, Anders, Mirbaha, Hilda, Mohila, Carrie A., Stevens, Todd M., Stone, Sara L., Yan, Wenzhong, Haeri, Mohammad, Chen, Xiang 'Anthony'
As Artificial Intelligence (AI) making advancements in medical decision-making, there is a growing need to ensure doctors develop appropriate reliance on AI to avoid adverse outcomes. However, existing methods in enabling appropriate AI reliance migh
Externí odkaz:
http://arxiv.org/abs/2404.04485
Autor:
Gu, Hongyan, Yan, Zihan, Alvi, Ayesha, Day, Brandon, Yang, Chunxu, Wu, Zida, Magaki, Shino, Haeri, Mohammad, Chen, Xiang 'Anthony'
The expansion of artificial intelligence (AI) in pathology tasks has intensified the demand for doctors' annotations in AI development. However, collecting high-quality annotations from doctors is costly and time-consuming, creating a bottleneck in A
Externí odkaz:
http://arxiv.org/abs/2404.01656
Autor:
Ghavami, Mahsa, Bakhshayesh, Babak Ghaffarzadeh, Haeri, Mohammad, Como, Giacomo, Kebriaei, Hamed
This paper introduces a consensus-based generalized multi-population aggregative game coordination approach with application to electric vehicles charging under transmission line constraints. The algorithm enables agents to seek an equilibrium soluti
Externí odkaz:
http://arxiv.org/abs/2310.11983
Autor:
Gu, Hongyan, Yang, Chunxu, Haeri, Mohammad, Wang, Jing, Tang, Shirley, Yan, Wenzhong, He, Shujin, Williams, Christopher Kazu, Magaki, Shino, Chen, Xiang 'Anthony'
Artificial Intelligence (AI) brings advancements to support pathologists in navigating high-resolution tumor images to search for pathology patterns of interest. However, existing AI-assisted tools have not realized this promised potential due to a l
Externí odkaz:
http://arxiv.org/abs/2302.07309
Autor:
Gu, Hongyan, Haeri, Mohammad, Ni, Shuo, Williams, Christopher Kazu, Zarrin-Khameh, Neda, Magaki, Shino, Chen, Xiang 'Anthony'
This work presents a mitosis detection method with only one vanilla Convolutional Neural Network (CNN). Our method consists of two steps: given an image, we first apply a CNN using a sliding window technique to extract patches that have mitoses; we t
Externí odkaz:
http://arxiv.org/abs/2208.12437
Autor:
Wang, Shengran, Greenbaum, Jonathan, Qiu, Chuan, Swerdlow, Russell H., Haeri, Mohammad, Gong, Yun, Shen, Hui, Xiao, Hongmei, Deng, Hongwen
Publikováno v:
In Genes & Diseases November 2024 11(6)
Autor:
Adelipour, Saeed, Haeri, Mohammad
Publikováno v:
In Computers and Electrical Engineering May 2024 116
Autor:
Gu, Hongyan, Liang, Yuan, Xu, Yifan, Williams, Christopher Kazu, Magaki, Shino, Khanlou, Negar, Vinters, Harry, Chen, Zesheng, Ni, Shuo, Yang, Chunxu, Yan, Wenzhong, Zhang, Xinhai Robert, Li, Yang, Haeri, Mohammad, Chen, Xiang 'Anthony'
Recent developments in AI have provided assisting tools to support pathologists' diagnoses. However, it remains challenging to incorporate such tools into pathologists' practice; one main concern is AI's insufficient workflow integration with medical
Externí odkaz:
http://arxiv.org/abs/2006.12683
Akademický článek
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In this paper, we discuss the controllability of a family of linear time-invariant (LTI) networks defined on a signed graph. In this direction, we introduce the notion of positive and negative signed zero forcing sets for the controllability analysis
Externí odkaz:
http://arxiv.org/abs/1908.05732