An efficient fluorescence in situ hybridization (FISH)-based circulating genetically abnormal cells (CACs) identification method based on Multi-scale MobileNet-YOLO-V4
Author:
Publisher
AME Publishing Company
Subject
Radiology, Nuclear Medicine and imaging
Link
https://qims.amegroups.com/article/download/90981/pdf
Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
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2. Circulating Genetically Abnormal Cells Identification Based on Improved Mask R-CNN and Signal Detection Model;2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL);2024-04-19
3. A Lightweight Deep Learning Framework for Identifying Circulating Genetically Abnormal Cells in Four-color Fluorescence in Situ Hybridization Images;2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL);2024-04-19
4. Mobilenet V3-transformer, a lightweight model for image caption;International Journal of Computers and Applications;2024-03-22
5. A Lightweight and Robust Framework for Circulating Genetically Abnormal Cells (CACs) Identification Using 4-Color Fluorescence In Situ Hybridization (FISH) Image and Deep Refined Learning;Journal of Digital Imaging;2023-05-25
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