Zobrazeno 1 - 10
of 465
pro vyhledávání: '"Luo Xiaoyuan"'
Autor:
Fu, Kexue, Luo, Xiaoyuan, Qu, Linhao, Wang, Shuo, Xiong, Ying, Maglogiannis, Ilias, Gao, Longxiang, Wang, Manning
The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in
Externí odkaz:
http://arxiv.org/abs/2409.19720
Autor:
Luo, Xiaoyuan, Nandurdikar, Vijay, Sangri-Yi, Revell, Alistair, Fourtakas, Georgios, Harish, Ajay B.
In this paper we propose a numerical procedure for the quantification of uncertainties in wave-structure interaction. We utilise the smoothed particle hydrodynamics (SPH) scheme for modelling the wave mechanics, coupled one-way with a finite element
Externí odkaz:
http://arxiv.org/abs/2408.10784
Multiple instance learning (MIL) problem is currently solved from either bag-classification or instance-classification perspective, both of which ignore important information contained in some instances and result in limited performance. For example,
Externí odkaz:
http://arxiv.org/abs/2408.04813
Publikováno v:
Zhongguo Jianchuan Yanjiu, Vol 15, Iss 4, Pp 153-158 (2020)
[Objectives] To comprehensively analyze the overall hydrodynamic performance and strength of the shape structure of a rim drive thruster(RDT),a method based on fluid structure interaction was proposed to solve the problem simultaneously.[Methods] Fir
Externí odkaz:
https://doaj.org/article/946ed6443307449ca7a02cf9b028b81b
Autor:
Wang, Shuo, Zhu, Yan, Luo, Xiaoyuan, Yang, Zhiwei, Zhang, Yizhe, Fu, Peiyao, Wang, Manning, Song, Zhijian, Li, Quanlin, Zhou, Pinghong, Guo, Yike
The development of artificial intelligence systems for colonoscopy analysis often necessitates expert-annotated image datasets. However, limitations in dataset size and diversity impede model performance and generalisation. Image-text colonoscopy rec
Externí odkaz:
http://arxiv.org/abs/2310.11173
Weakly supervised whole slide image classification is usually formulated as a multiple instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut out of it are treated as instances. Existing methods either train an ins
Externí odkaz:
http://arxiv.org/abs/2307.02249
This paper introduces the novel concept of few-shot weakly supervised learning for pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed based on prompt learning and the utilization of a large language model, GPT-4
Externí odkaz:
http://arxiv.org/abs/2305.17891
3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect c
Externí odkaz:
http://arxiv.org/abs/2211.04696
Computer-aided pathology diagnosis based on the classification of Whole Slide Image (WSI) plays an important role in clinical practice, and it is often formulated as a weakly-supervised Multiple Instance Learning (MIL) problem. Existing methods solve
Externí odkaz:
http://arxiv.org/abs/2210.03664
Multiple Instance Learning (MIL) is widely used in analyzing histopathological Whole Slide Images (WSIs). However, existing MIL methods do not explicitly model the data distribution, and instead they only learn a bag-level or instance-level decision
Externí odkaz:
http://arxiv.org/abs/2206.08861