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Nuclei Segmentation in Hematoxylin and Eosin (H&E)-Stained Histopathological Images Using a Deep Neural Network., , , , , , , , , and 1 other author(s). SIU, page 1-5. IEEE, (2020)Cascade R-CNN for MIDOG Challenge., , , , and . MIDOG/MOOD/Learn2Reg@MICCAI, volume 13166 of Lecture Notes in Computer Science, page 81-85. Springer, (2021)Automated cell segmentation and spot detection in fluorescence in situ hybridization staining to assess HER2 status in breast cancer., , , , , , , , , and . SIU, page 1-4. IEEE, (2018)Mitosis domain generalization in histopathology images - The MIDOG challenge., , , , , , , , , and 31 other author(s). Medical Image Anal., (2023)Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network., , and . Comput. Biol. Medicine, (2019)Segmentation of the main structures in Hematoxylin and Eosin images., , , , , , , , , and 1 other author(s). SIU, page 1-4. IEEE, (2018)Plant classification using group of features., and . SIU, page 1957-1960. IEEE, (2016)Using convolutional neural networks for plant classification., and . SIU, page 1-4. IEEE, (2017)Mitosis domain generalization in histopathology images - The MIDOG challenge., , , , , , , , , and 25 other author(s). CoRR, (2022)An Automated and Accurate Methodology to Assess Ki-67 Labeling Index of Immunohistochemical Staining Images of Breast Cancer Tissues., , , , , , , and . IWSSIP, page 1-5. IEEE, (2018)