GradMix for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-16434-7_17
Reference19 articles.
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2. Graham, S., et al.: HoVer-Net: simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med. Image Anal. 58, 101563 (2019)
3. Raza, S.E.A., et al.: Micro-Net: a unified model for segmentation of various objects in microscopy images. Med. Image Anal. 52, 160–173 (2019)
4. Vu, Q.D., et al.: Methods for segmentation and classification of digital microscopy tissue images. Front. Bioeng. Biotechnol. 7, 53 (2019)
5. Lecture Notes in Computer Science;Y Zhou,2019
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1. DAX-Net: A dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images;Computer Methods and Programs in Biomedicine;2024-05
2. A novel cost function for nuclei segmentation and classification in imbalanced histopathology data-sets;Computerized Medical Imaging and Graphics;2023-10
3. Image Analysis of Nuclei Histopathology Using Deep Learning: A Review of Segmentation, Detection, and Classification;SN Computer Science;2023-09-12
4. DiffMix: Diffusion Model-Based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets;Lecture Notes in Computer Science;2023
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