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Titlebook: ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging; Select Proceedings Anubha Gupta,Ritu Gupta Conference proceedings 2019 Sp

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发表于 2025-3-21 18:26:38 | 显示全部楼层 |阅读模式
书目名称ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging
副标题Select Proceedings
编辑Anubha Gupta,Ritu Gupta
视频video
概述Comprises select contribution from the IEEE ISBI challenge on white blood cancer imaging.Presents results obtained from more than 14000 cancer cell images.Discusses potential low-cost medical imaging
丛书名称Lecture Notes in Bioengineering
图书封面Titlebook: ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging; Select Proceedings Anubha Gupta,Ritu Gupta Conference proceedings 2019 Sp
描述.This book comprises select peer-reviewed proceedings of the medical challenge - C-NMC challenge: Classification of normal versus malignant cells in B-ALL white blood cancer microscopic images. The challenge was run as part of the IEEE International Symposium on Biomedical Imaging (IEEE ISBI) 2019 held at Venice, Italy in April 2019. Cell classification via image processing has recently gained interest from the point of view of building computer-assisted diagnostic tools for blood disorders such as leukaemia. In order to arrive at a conclusive decision on disease diagnosis and degree of progression, it is very important to identify malignant cells with high accuracy. Computer-assisted tools can be very helpful in automating the process of cell segmentation and identification because morphologically both cell types appear similar. This particular challenge was run on a curated data set of more than 14000 cell images of very high quality. More than 200 international teams participated in the challenge. This book covers various solutions using machine learning and deep learning approaches. The book will prove useful for academics, researchers, and professionals interested in building
出版日期Conference proceedings 2019
关键词Biomedical imaging; Cancer imaging; Deep learning; Signal processing; Computer assisted diagnosis; ISBI 2
版次1
doihttps://doi.org/10.1007/978-981-15-0798-4
isbn_softcover978-981-15-0800-4
isbn_ebook978-981-15-0798-4Series ISSN 2195-271X Series E-ISSN 2195-2728
issn_series 2195-271X
copyrightSpringer Nature Singapore Pte Ltd. 2019
The information of publication is updating

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发表于 2025-3-21 20:54:44 | 显示全部楼层
Classification of Normal and Leukemic Blast Cells in B-ALL Cancer Using a Combination of Convolutiosed classifier has been validated using multiple experiments. Our approach is able to achieve substantial performance gains when compared to, conventional, stand-alone CNN- and RNN-based methods. The highest accuracy achieved by our model is 86.6%.
发表于 2025-3-22 03:53:20 | 显示全部楼层
Neighborhood-Correction Algorithm for Classification of Normal and Malignant Cells,ults demonstrate that our proposed NCA achieves the weighted F1-score of 92.50% and balanced accuracy of 91.73% in the preliminary testing and achieves weighted F1-score of 91.04% in the final testing, which ranks the first in C-NMC. Associated code is available at ..
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DeepMEN: Multi-model Ensemble Network for B-Lymphoblast Cell Classification,hnology to fuse the six models. In addition, we use the pseudo-label and Test Time Augmentator (TTA) to reduce covariance shifts caused by individual differences. Finally, we obtained a weighted F1-score of 0.903 in the preliminary test set and 0.8856 in the final test set.
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Atmika Honnalgere,Gaurav Nayakand temperature are pointed out. Furthermore, we show how selectivity can be manipulated by the choice of buffer electrolyte. Here, the most effective parameters such as pH, ionic strength, buffer composition, complex formation and organic modifiers are discussed.
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