Zobrazeno 1 - 10
of 613
pro vyhledávání: '"Yang, Guanyu"'
Few-shot Class Incremental Learning (FSCIL) presents a challenging yet realistic scenario, which requires the model to continually learn new classes with limited labeled data (i.e., incremental sessions) while retaining knowledge of previously learne
Externí odkaz:
http://arxiv.org/abs/2411.01172
The precise subtype classification of myeloproliferative neoplasms (MPNs) based on multimodal information, which assists clinicians in diagnosis and long-term treatment plans, is of great clinical significance. However, it remains a great challenging
Externí odkaz:
http://arxiv.org/abs/2407.08167
Publikováno v:
2018, Neural Information Processing - 25th International Conference, ICONIP
Due to the huge category number, the sophisticated combinations of various strokes and radicals, and the free writing or printing styles, generating Chinese characters with diverse styles is always considered as a difficult task. In this paper, an ef
Externí odkaz:
http://arxiv.org/abs/2406.06122
Publikováno v:
International Conference on Brain Inspired Cognitive Systems 2023
Synthesizing Chinese characters with consistent style using few stylized examples is challenging. Existing models struggle to generate arbitrary style characters with limited examples. In this paper, we propose the Generalized W-Net, a novel class of
Externí odkaz:
http://arxiv.org/abs/2406.06847
Autor:
Wu, Fuzhi, Wu, Jiasong, Kong, Youyong, Yang, Chunfeng, Yang, Guanyu, Shu, Huazhong, Carrault, Guy, Senhadji, Lotfi
Deep learning and Convolutional Neural Networks (CNNs) have driven major transformations in diverse research areas. However, their limitations in handling low-frequency information present obstacles in certain tasks like interpreting global structure
Externí odkaz:
http://arxiv.org/abs/2403.08157
Autor:
Zhang, Yuan, Qi, Yaolei, Qi, Xiaoming, Senhadji, Lotfi, Wei, Yongyue, Chen, Feng, Yang, Guanyu
Federated learning (FL) for histopathology image segmentation involving multiple medical sites plays a crucial role in advancing the field of accurate disease diagnosis and treatment. However, it is still a task of great challenges due to the sample
Externí odkaz:
http://arxiv.org/abs/2312.12824
Autor:
Zhao, Weiguang, Yang, Guanyu, Zhang, Rui, Jiang, Chenru, Yang, Chaolong, Yan, Yuyao, Hussain, Amir, Huang, Kaizhu
With the explosive 3D data growth, the urgency of utilizing zero-shot learning to facilitate data labeling becomes evident. Recently, methods transferring language or language-image pre-training models like Contrastive Language-Image Pre-training (CL
Externí odkaz:
http://arxiv.org/abs/2312.07039
Accurate segmentation of topological tubular structures, such as blood vessels and roads, is crucial in various fields, ensuring accuracy and efficiency in downstream tasks. However, many factors complicate the task, including thin local structures a
Externí odkaz:
http://arxiv.org/abs/2307.08388
The foundation models based on pre-training technology have significantly advanced artificial intelligence from theoretical to practical applications. These models have facilitated the feasibility of computer-aided diagnosis for widespread use. Medic
Externí odkaz:
http://arxiv.org/abs/2307.07246
Coronary artery segmentation on coronary-computed tomography angiography (CCTA) images is crucial for clinical use. Due to the expertise-required and labor-intensive annotation process, there is a growing demand for the relevant label-efficient learn
Externí odkaz:
http://arxiv.org/abs/2307.04472