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pro vyhledávání: '"GUPTA, RAVI"'
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
Gene selection plays a pivotal role in oncology research for improving outcome prediction accuracy and facilitating cost-effective genomic profiling for cancer patients. This paper introduces two gene selection strategies for deep learning-based surv
Externí odkaz:
http://arxiv.org/abs/2403.01927
Autor:
Parulekar, Amruta, Kanwat, Utkarsh, Gupta, Ravi Kant, Chippa, Medha, Jacob, Thomas, Bameta, Tripti, Rane, Swapnil, Sethi, Amit
Segmentation and classification of cell nuclei in histopathology images using deep neural networks (DNNs) can save pathologists' time for diagnosing various diseases, including cancers, by automating cell counting and morphometric assessments. It is
Externí odkaz:
http://arxiv.org/abs/2310.03346
This paper presents a novel approach for unsupervised domain adaptation (UDA) targeting H&E stained histology images. Existing adversarial domain adaptation methods may not effectively align different domains of multimodal distributions associated wi
Externí odkaz:
http://arxiv.org/abs/2309.17172