Zobrazeno 1 - 10
of 86
pro vyhledávání: '"Duan Wenting"'
Autor:
Lou, Zhenye, Xu, Qing, Jiang, Zekun, He, Xiangjian, Chen, Zhen, Wang, Yi, Li, Chenxin, He, Maggie M., Duan, Wenting
Domain-generalized nuclei segmentation refers to the generalizability of models to unseen domains based on knowledge learned from source domains and is challenged by various image conditions, cell types, and stain strategies. Recently, the Segment An
Externí odkaz:
http://arxiv.org/abs/2408.11787
ESP-MedSAM: Efficient Self-Prompting SAM for Universal Domain-Generalized Medical Image Segmentation
Autor:
Xu, Qing, Li, Jiaxuan, He, Xiangjian, Liu, Ziyu, Chen, Zhen, Duan, Wenting, Li, Chenxin, He, Maggie M., Tesema, Fiseha B., Cheah, Wooi P., Wang, Yi, Qu, Rong, Garibaldi, Jonathan M.
The universality of deep neural networks across different modalities and their generalization capabilities to unseen domains play an essential role in medical image segmentation. The recent Segment Anything Model (SAM) has demonstrated its potential
Externí odkaz:
http://arxiv.org/abs/2407.14153
Image segmentation plays an essential role in nuclei image analysis. Recently, the segment anything model has made a significant breakthrough in such tasks. However, the current model exists two major issues for cell segmentation: (1) the image encod
Externí odkaz:
http://arxiv.org/abs/2308.12231
Autor:
Xu, Qing, Duan, Wenting
Chest radiographs are the most commonly performed radiological examinations for lesion detection. Recent advances in deep learning have led to encouraging results in various thoracic disease detection tasks. Particularly, the architecture with featur
Externí odkaz:
http://arxiv.org/abs/2306.13813
Deep learning architecture with convolutional neural network (CNN) achieves outstanding success in the field of computer vision. Where U-Net, an encoder-decoder architecture structured by CNN, makes a great breakthrough in biomedical image segmentati
Externí odkaz:
http://arxiv.org/abs/2202.00972
Publikováno v:
BrainLes 2020. Lecture Notes in Computer Science, vol 12659, pp 410-419
In this paper we propose a 2D deep residual Unet with 104 convolutional layers (DR-Unet104) for lesion segmentation in brain MRIs. We make multiple additions to the Unet architecture, including adding the 'bottleneck' residual block to the Unet encod
Externí odkaz:
http://arxiv.org/abs/2011.02840
Autor:
Yan, Junling, Duan, Wenting, Gao, Qinhan, Mao, Tianxiao, Wang, Majie, Duan, Jialin, Li, Jiankang
Publikováno v:
In Pharmacological Research September 2023 195
Publikováno v:
In Computers in Biology and Medicine March 2023 154
Publikováno v:
In Colloids and Surfaces A: Physicochemical and Engineering Aspects 5 October 2022 650