Zobrazeno 1 - 10
of 533
pro vyhledávání: '"Lai Maode"'
Prompt tuning methods have achieved remarkable success in parameter-efficient fine-tuning on large pre-trained models. However, their application to dual-modal fusion-based visual-language pre-trained models (VLPMs), such as GLIP, has encountered iss
Externí odkaz:
http://arxiv.org/abs/2407.11414
Large models have become mainstream, yet their applications in digital pathology still require exploration. Meanwhile renal pathology images play an important role in the diagnosis of renal diseases. We conducted image segmentation and paired corresp
Externí odkaz:
http://arxiv.org/abs/2406.18556
The annotation of digital pathological slide data for renal cell carcinoma is of paramount importance for correct diagnosis of artificial intelligence models due to the heterogeneous nature of the tumor. This process not only facilitates a deeper und
Externí odkaz:
http://arxiv.org/abs/2403.11211
Autor:
Song, Zhiyun, Du, Penghui, Yan, Junpeng, Li, Kailu, Shou, Jianzhong, Lai, Maode, Fan, Yubo, Xu, Yan
Self-supervised pretraining attempts to enhance model performance by obtaining effective features from unlabeled data, and has demonstrated its effectiveness in the field of histopathology images. Despite its success, few works concentrate on the ext
Externí odkaz:
http://arxiv.org/abs/2309.07394
Autor:
Wu, Yongjian, Zhou, Yang, Saiyin, Jiya, Wei, Bingzheng, Lai, Maode, Shou, Jianzhong, Fan, Yubo, Xu, Yan
Large-scale visual-language pre-trained models (VLPM) have proven their excellent performance in downstream object detection for natural scenes. However, zero-shot nuclei detection on H\&E images via VLPMs remains underexplored. The large gap between
Externí odkaz:
http://arxiv.org/abs/2306.17659
Autor:
Zhou, Yang, Wu, Yongjian, Wang, Zihua, Wei, Bingzheng, Lai, Maode, Shou, Jianzhong, Fan, Yubo, Xu, Yan
Nuclei instance segmentation on histopathology images is of great clinical value for disease analysis. Generally, fully-supervised algorithms for this task require pixel-wise manual annotations, which is especially time-consuming and laborious for th
Externí odkaz:
http://arxiv.org/abs/2306.02691
Transformer based multiple instance learning for weakly supervised histopathology image segmentation
Hispathological image segmentation algorithms play a critical role in computer aided diagnosis technology. The development of weakly supervised segmentation algorithm alleviates the problem of medical image annotation that it is time-consuming and la
Externí odkaz:
http://arxiv.org/abs/2205.08878
Publikováno v:
In Translational Research August 2024 270:81-93
Autor:
Li, Yige, Gong, Jingwen, Sun, Qingrong, Vong, Eu Gene, Cheng, Xiaoqing, Wang, Binghong, Yuan, Ying, Jin, Li, Gamazon, Eric R., Zhou, Dan, Lai, Maode, Zhang, Dandan
Publikováno v:
In The American Journal of Human Genetics 7 March 2024 111(3):562-583
Publikováno v:
In Chemical Physics Letters 16 February 2024 837