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pro vyhledávání: '"Sekhar, Ardhendu"'
Autor:
Sekhar, Ardhendu, Bhattacharya, Aditya, Goyal, Vinayak, Goel, Vrinda, Bhangale, Aditya, Gupta, Ravi Kant, Sethi, Amit
In this study, we investigate the performance of few-shot classification models across different domains, specifically natural images and histopathological images. We first train several few-shot classification models on natural images and evaluate t
Externí odkaz:
http://arxiv.org/abs/2410.09176
Autor:
Sekhar, Ardhendu, Goel, Vrinda, Jain, Garima, Patil, Abhijeet, Gupta, Ravi Kant, Bameta, Tripti, Rane, Swapnil, Sethi, Amit
The current standard for detecting human epidermal growth factor receptor 2 (HER2) status in breast cancer patients relies on HER2 amplification, identified through fluorescence in situ hybridization (FISH) or immunohistochemistry (IHC). However, hem
Externí odkaz:
http://arxiv.org/abs/2408.13818
Publikováno v:
In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies, Volume 1, 2024, ISBN 978-989-758-688-0, ISSN 2184-4305, pp. 244-253
This paper presents a study on few-shot classification in the context of histopathology images. While few-shot learning has been studied for natural image classification, its application to histopathology is relatively unexplored. Given the scarcity
Externí odkaz:
http://arxiv.org/abs/2408.13816
Training a computer vision system to segment a novel class typically requires collecting and painstakingly annotating lots of images with objects from that class. Few-shot segmentation techniques reduce the required number of images to learn to segme
Externí odkaz:
http://arxiv.org/abs/2403.15089
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