Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Peng, Linkai"'
Autor:
Peng, Linkai, Zhang, Zheyuan, Durak, Gorkem, Miller, Frank H., Medetalibeyoglu, Alpay, Wallace, Michael B., Bagci, Ulas
Pancreatic cancer remains one of the leading causes of cancer-related mortality worldwide. Precise segmentation of pancreatic tumors from medical images is a bottleneck for effective clinical decision-making. However, achieving a high accuracy is oft
Externí odkaz:
http://arxiv.org/abs/2407.19284
Large language models (LLMs), renowned for their powerful conversational abilities, are widely recognized as exceptional tools in the field of education, particularly in the context of automated intelligent instruction systems for language learning.
Externí odkaz:
http://arxiv.org/abs/2407.09209
Autor:
Zhang, Zheyuan, Keles, Elif, Durak, Gorkem, Taktak, Yavuz, Susladkar, Onkar, Gorade, Vandan, Jha, Debesh, Ormeci, Asli C., Medetalibeyoglu, Alpay, Yao, Lanhong, Wang, Bin, Isler, Ilkin Sevgi, Peng, Linkai, Pan, Hongyi, Vendrami, Camila Lopes, Bourhani, Amir, Velichko, Yury, Gong, Boqing, Spampinato, Concetto, Pyrros, Ayis, Tiwari, Pallavi, Klatte, Derk C. F., Engels, Megan, Hoogenboom, Sanne, Bolan, Candice W., Agarunov, Emil, Harfouch, Nassier, Huang, Chenchan, Bruno, Marco J., Schoots, Ivo, Keswani, Rajesh N., Miller, Frank H., Gonda, Tamas, Yazici, Cemal, Tirkes, Temel, Turkbey, Baris, Wallace, Michael B., Bagci, Ulas
Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, la
Externí odkaz:
http://arxiv.org/abs/2405.12367
People have long hoped for a conversational system that can assist in real-life situations, and recent progress on large language models (LLMs) is bringing this idea closer to reality. While LLMs are often impressive in performance, their efficacy in
Externí odkaz:
http://arxiv.org/abs/2308.14536
Autor:
Lin, Li, Peng, Linkai, He, Huaqing, Cheng, Pujin, Wu, Jiewei, Wong, Kenneth K. Y., Tang, Xiaoying
Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown promising performance, pa
Externí odkaz:
http://arxiv.org/abs/2212.05566
Mispronunciation detection and diagnosis (MDD) technology is a key component of computer-assisted pronunciation training system (CAPT). In the field of assessing the pronunciation quality of constrained speech, the given transcriptions can play the r
Externí odkaz:
http://arxiv.org/abs/2206.07289
Multi-modal magnetic resonance (MR) imaging provides great potential for diagnosing and analyzing brain gliomas. In clinical scenarios, common MR sequences such as T1, T2 and FLAIR can be obtained simultaneously in a single scanning process. However,
Externí odkaz:
http://arxiv.org/abs/2203.04586
Unsupervised domain adaptation has been proposed recently to tackle the so-called domain shift between training data and test data with different distributions. However, most of them only focus on single-target domain adaptation and cannot be applied
Externí odkaz:
http://arxiv.org/abs/2203.03631
Various deep learning models have been developed to segment anatomical structures from medical images, but they typically have poor performance when tested on another target domain with different data distribution. Recently, unsupervised domain adapt
Externí odkaz:
http://arxiv.org/abs/2201.04812
Publikováno v:
In Materials Today Energy December 2023 38