Zobrazeno 1 - 10
of 5 486
pro vyhledávání: '"Yen, Wei"'
Semi-supervised learning (SSL) for medical image segmentation is a challenging yet highly practical task, which reduces reliance on large-scale labeled dataset by leveraging unlabeled samples. Among SSL techniques, the weak-to-strong consistency fram
Externí odkaz:
http://arxiv.org/abs/2410.13486
The advancement of Spatial Transcriptomics (ST) has facilitated the spatially-aware profiling of gene expressions based on histopathology images. Although ST data offers valuable insights into the micro-environment of tumors, its acquisition cost rem
Externí odkaz:
http://arxiv.org/abs/2409.15092
Autor:
Dong, Jiahua, Zhang, Yue, Wang, Qiuli, Tong, Ruofeng, Ying, Shihong, Gong, Shaolin, Zhang, Xuanpu, Lin, Lanfen, Chen, Yen-Wei, Zhou, S. Kevin
Medical image segmentation is crucial in the field of medical imaging, aiding in disease diagnosis and surgical planning. Most established segmentation methods rely on supervised deep learning, in which clean and precise labels are essential for supe
Externí odkaz:
http://arxiv.org/abs/2409.05024
Autor:
Ouyang, Shuyi, Zhang, Jinyang, Lin, Xiangye, Wang, Xilai, Chen, Qingqing, Chen, Yen-Wei, Lin, Lanfen
Conventional medical image segmentation methods have been found inadequate in facilitating physicians with the identification of specific lesions for diagnosis and treatment. Given the utility of text as an instructional format, we introduce a novel
Externí odkaz:
http://arxiv.org/abs/2408.17347
In recent years, large-scale multimodal models have demonstrated impressive capabilities across various domains. However, enabling these models to effectively perform multiple multimodal tasks simultaneously remains a significant challenge. To addres
Externí odkaz:
http://arxiv.org/abs/2408.03001
Autor:
Ouyang, Shuyi, Wang, Hongyi, Niu, Ziwei, Bai, Zhenjia, Xie, Shiao, Xu, Yingying, Tong, Ruofeng, Chen, Yen-Wei, Lin, Lanfen
Publikováno v:
Proceedings of the 31st ACM International Conference on Multimedia. 2023: 4768-4777
The task of multi-label image classification involves recognizing multiple objects within a single image. Considering both valuable semantic information contained in the labels and essential visual features presented in the image, tight visual-lingui
Externí odkaz:
http://arxiv.org/abs/2407.16244
This work reports the procedure for modeling piezoelectric acoustic resonators and filters at millimeter wave (mmWave). Different from conventional methods for lower frequency piezoelectric devices, we include both acoustic and electromagnetic (EM) e
Externí odkaz:
http://arxiv.org/abs/2405.19591
Recent advancements in large-scale models have showcased remarkable generalization capabilities in various tasks. However, integrating multimodal processing into these models presents a significant challenge, as it often comes with a high computation
Externí odkaz:
http://arxiv.org/abs/2403.05060
Autor:
Sen, Mrinmay, Qin, A. K., C, Gayathri, N, Raghu Kishore, Chen, Yen-Wei, Raman, Balasubramanian
This paper introduces a new stochastic optimization method based on the regularized Fisher information matrix (FIM), named SOFIM, which can efficiently utilize the FIM to approximate the Hessian matrix for finding Newton's gradient update in large-sc
Externí odkaz:
http://arxiv.org/abs/2403.02833
Autor:
Tam, Zhi-Rui, Pai, Ya-Ting, Lee, Yen-Wei, Chen, Jun-Da, Chu, Wei-Min, Cheng, Sega, Shuai, Hong-Han
We present TMMLU+, a new benchmark designed for Traditional Chinese language understanding. TMMLU+ is a multi-choice question-answering dataset with 66 subjects from elementary to professional level. It is six times larger and boasts a more balanced
Externí odkaz:
http://arxiv.org/abs/2403.01858