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pro vyhledávání: '"Min, Seonghui"'
In multi-class histopathology nuclei analysis tasks, the lack of training data becomes a main bottleneck for the performance of learning-based methods. To tackle this challenge, previous methods have utilized generative models to increase data by gen
Externí odkaz:
http://arxiv.org/abs/2407.14434
With the emergence of the Segment Anything Model (SAM) as a foundational model for image segmentation, its application has been extensively studied across various domains, including the medical field. However, its potential in the context of histopat
Externí odkaz:
http://arxiv.org/abs/2310.10493
Autor:
Min, Seonghui, Jeong, Won-Ki
Tumor region segmentation is an essential task for the quantitative analysis of digital pathology. Recently presented deep neural networks have shown state-of-the-art performance in various image-segmentation tasks. However, because of the unclear bo
Externí odkaz:
http://arxiv.org/abs/2307.01015
The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024. The 2387 papers
Autor:
M. Emre Celebi, Md Sirajus Salekin, Hyunwoo Kim, Shadi Albarqouni, Catarina Barata, Allan Halpern, Philipp Tschandl, Marc Combalia, Yuan Liu, Ghada Zamzmi, Joshua Levy, Huzefa Rangwala, Annika Reinke, Diya Wynn, Bennett Landman, Won-Ki Jeong, Yiqing Shen, Zhongying Deng, Spyridon Bakas, Xiaoxiao Li, Chen Qin, Nicola Rieke, Holger Roth, Daguang Xu
This double volume set LNCS 14393-14394 constitutes the proceedings from the workshops held at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023 Workshops, which took place in Vancouver, BC,