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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