Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Pan, Liangrui"'
Autor:
Pan, Liangrui, Peng, Yijun, Li, Yan, Liang, Yiyi, Xu, Liwen, Liang, Qingchun, Peng, Shaoliang
Integrating the different data modalities of cancer patients can significantly improve the predictive performance of patient survival. However, most existing methods ignore the simultaneous utilization of rich semantic features at different scales in
Externí odkaz:
http://arxiv.org/abs/2405.07702
Opportunities and challenges in the application of large artificial intelligence models in radiology
Autor:
Pan, Liangrui, Zhao, Zhenyu, Lu, Ying, Tang, Kewei, Fu, Liyong, Liang, Qingchun, Peng, Shaoliang
Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development. As people enjoy the convenience by this AI large model, more and more large models in subdivided fields are grad
Externí odkaz:
http://arxiv.org/abs/2403.16112
Autor:
Pan, Liangrui, Peng, Yijun, Li, Yan, Wang, Xiang, Liu, Wenjuan, Xu, Liwen, Liang, Qingchun, Peng, Shaoliang
Accurately predicting the survival rate of cancer patients is crucial for aiding clinicians in planning appropriate treatment, reducing cancer-related medical expenses, and significantly enhancing patients' quality of life. Multimodal prediction of c
Externí odkaz:
http://arxiv.org/abs/2403.09290
Histopathology image segmentation is the gold standard for diagnosing cancer, and can indicate cancer prognosis. However, histopathology image segmentation requires high-quality masks, so many studies now use imagelevel labels to achieve pixel-level
Externí odkaz:
http://arxiv.org/abs/2308.10449
Histopathological images are the gold standard for diagnosing liver cancer. However, the accuracy of fully digital diagnosis in computational pathology needs to be improved. In this paper, in order to solve the problem of multi-label and low classifi
Externí odkaz:
http://arxiv.org/abs/2308.10446
Due to the high heterogeneity and clinical characteristics of cancer, there are significant differences in multi-omic data and clinical characteristics among different cancer subtypes. Therefore, accurate classification of cancer subtypes can help do
Externí odkaz:
http://arxiv.org/abs/2308.10917
Autor:
Pan, Liangrui, Liu, Dazhen, Dou, Yutao, Wang, Lian, Feng, Zhichao, Rong, Pengfei, Xu, Liwen, Peng, Shaoliang
Due to the high heterogeneity and clinical characteristics of cancer, there are significant differences in multi-omics data and clinical features among subtypes of different cancers. Therefore, the identification and discovery of cancer subtypes are
Externí odkaz:
http://arxiv.org/abs/2307.04075
Colorectal cancer (CRC) is among the top three malignant tumor types in terms of morbidity and mortality. Histopathological images are the gold standard for diagnosing colon cancer. Cellular nuclei instance segmentation and classification, and nuclea
Externí odkaz:
http://arxiv.org/abs/2210.10981
Computational pathology is part of precision oncology medicine. The integration of high-throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics into clinical practice improves cancer treatment plans, tr
Externí odkaz:
http://arxiv.org/abs/2206.01728
Autor:
Pan, Liangrui, Wang, Hetian, Wang, Lian, Ji, Boya, Liu, Mingting, Chongcheawchamnan, Mitchai, Yuan, Jin, Peng, Shaoliang
The degree of malignancy of osteosarcoma and its tendency to metastasize/spread mainly depend on the pathological grade (determined by observing the morphology of the tumor under a microscope). The purpose of this study is to use artificial intellige
Externí odkaz:
http://arxiv.org/abs/2204.13838