Superpixel Segmentation of Breast Cancer Pathology Images Based on Features Extracted from the Autoencoder
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Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/8892590/8905245/08905358.pdf?arnumber=8905358
Cited by 6 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
1. Advancing Tumor Cell Classification and Segmentation in Ki-67 Images: A Systematic Review of Deep Learning Approaches;Lecture Notes in Networks and Systems;2024
2. Novel Arc-Cost Functions and Seed Relevance Estimations for Compact and Accurate Superpixels;Journal of Mathematical Imaging and Vision;2023-08-16
3. Application of Deep Learning in Histopathology Images of Breast Cancer: A Review;Micromachines;2022-12-11
4. H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images;Frontiers in Medicine;2022-09-14
5. Towards a Simple and Efficient Object-based Superpixel Delineation Framework;2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI);2021-10
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