Zobrazeno 1 - 10
of 184
pro vyhledávání: '"Gu Hongyan"'
Graph neural networks(GNNs) have a wide range of applications in multimedia.Recent studies have shown that Graph neural networks(GNNs) are vulnerable to link stealing attacks,which infers the existence of edges in the target GNN's training graph.Exis
Externí odkaz:
http://arxiv.org/abs/2410.02826
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:
Aubreville, Marc, Stathonikos, Nikolas, Donovan, Taryn A., Klopfleisch, Robert, Ganz, Jonathan, Ammeling, Jonas, Wilm, Frauke, Veta, Mitko, Jabari, Samir, Eckstein, Markus, Annuscheit, Jonas, Krumnow, Christian, Bozaba, Engin, Cayir, Sercan, Gu, Hongyan, Chen, Xiang 'Anthony', Jahanifar, Mostafa, Shephard, Adam, Kondo, Satoshi, Kasai, Satoshi, Kotte, Sujatha, Saipradeep, VG, Lafarge, Maxime W., Koelzer, Viktor H., Wang, Ziyue, Zhang, Yongbing, Yang, Sen, Wang, Xiyue, Breininger, Katharina, Bertram, Christof A.
Publikováno v:
Medical Image Analysis Volume 94, May 2024, 103155
Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with deterioration of algorithmic performance under shifts in image repres
Externí odkaz:
http://arxiv.org/abs/2309.15589
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:
Aubreville, Marc, Stathonikos, Nikolas, Donovan, Taryn A., Klopfleisch, Robert, Ammeling, Jonas, Ganz, Jonathan, Wilm, Frauke, Veta, Mitko, Jabari, Samir, Eckstein, Markus, Annuscheit, Jonas, Krumnow, Christian, Bozaba, Engin, Çayır, Sercan, Gu, Hongyan, Chen, Xiang ‘Anthony’, Jahanifar, Mostafa, Shephard, Adam, Kondo, Satoshi, Kasai, Satoshi, Kotte, Sujatha, Saipradeep, V.G., Lafarge, Maxime W., Koelzer, Viktor H., Wang, Ziyue, Zhang, Yongbing, Yang, Sen, Wang, Xiyue, Breininger, Katharina, Bertram, Christof A.
Publikováno v:
In Medical Image Analysis May 2024 94
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
Despite the promises of data-driven artificial intelligence (AI), little is known about how we can bridge the gulf between traditional physician-driven diagnosis and a plausible future of medicine automated by AI. Specifically, how can we involve AI
Externí odkaz:
http://arxiv.org/abs/2006.12695
Publikováno v:
In Australian Critical Care November 2023 36(6):933-939