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Nuclei classification provides valuable information for histopathology image analysis.
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2018
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H. D. Couture, L. A. Williams, J. Geradts, S. J. Nyante, E. N. Butler, J. Marron, C. M. Perou, M. A. Troester, and M. Niethammer, “Image analysis with deep learning to predict breast cancer grade, er status, histologic subtype, and intrinsic subtype,” NPJ breast cancer , vol. 4, no. 1, pp. 1–8, 2018
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S. S. Basha, S. Ghosh, K. K. Babu, S. R. Dubey, V. Pulabaigari, and S. Mukherjee, “Rccnet: An efficient convolutional neural network for histological routine colon cancer nuclei classification,” in 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) . IEEE, 2018, pp. 1222–1227
2018
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B. Zhao, X. Chen, Z. Li, Z. Yu, S. Yao, L. Yan, Y. Wang, Z. Liu, C. Liang, and C. Han, “Triple u-net: Hematoxylin-aware nuclei segmentation with progressive dense feature aggregation,” Medical Image Analysis , vol. 65, p. 101786, 2020
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2021
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2018
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S. Tabibu, P. Vinod, and C. Jawahar, “Pan-renal cell carcinoma classification and survival prediction from histopathology images using deep learning,” Scientific reports , vol. 9, no. 1, pp. 1–9, 2019
2019
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S. Graham, Q. D. Vu, S. E. A. Raza, A. Azam, Y. W. Tsang, J. T. Kwak, and N. Rajpoot, “Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images,” Medical Image Analysis , vol. 58, p. 101563, 2019
2019
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K. Zormpas-Petridis, H. Failmezger, S. E. A. Raza, I. Roxanis, Y. Jamin, and Y. Yuan, “Superpixel-based conditional random fields (supercrf): Incorporating global and local context for enhanced deep learning in melanoma histopathology,” Frontiers in oncology , vol. 9, p. 1045, 2019
2019
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Y. Zhou, S. Graham, N. Alemi Koohbanani, M. Shaban, P.-A. Heng, and N. Rajpoot, “Cgc-net: Cell graph convolutional network for grading of colorectal cancer histology images,” in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops , 2019, pp. 0–0
2019
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M. Fey and J. E. Lenssen, “Fast graph representation learning with pytorch geometric,” ICLR Workshop on Representation Learning on Graphs and Manifolds , 2019
2019
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F. Mahmood et al. , “Deep adversarial training for multi-organ nuclei segmentation in histopathology images,” IEEE Transactions on Medical Imaging , vol. 39, no. 11, pp. 3257–3267, 2019
2019
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A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dollár, “Panoptic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 9404–9413
2019
Cited alongside, same era.
M. Sureka, A. Patil, D. Anand, and A. Sethi, “Visualization for histopathology images using graph convolutional neural networks,” in IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE) . IEEE, 2020, pp. 331–335
2020
Cited alongside, same era.
2021
Later among the works it cites.
G. Jaume, P. Pati, B. Bozorgtabar, A. Foncubierta, A. M. Anniciello, F. Feroce, T. Rau, J.-P. Thiran, M. Gabrani, and O. Goksel, “Quantifying explainers of graph neural networks in computational pathology,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 8106–8116
2021
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L. Studer, J. Wallau, H. Dawson, I. Zlobec, and A. Fischer, “Classification of intestinal gland cell-graphs using graph neural networks,” in 25th International Conference on Pattern Recognition (ICPR) . IEEE, 2021, pp. 3636–3643
2021
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G. Li, M. Müller, G. Qian, I. C. D. Perez, A. Abualshour, A. K. Thabet, and B. Ghanem, “Deepgcns: Making gcns go as deep as cnns,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
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R. Verma, N. Kumar, A. Patil, N. C. Kurian, S. Rane, S. Graham, Q. D. Vu, M. Zwager, S. E. A. Raza, N. Rajpoot et al. , “Monusac2020: A multi-organ nuclei segmentation and classification challenge,” IEEE Transactions on Medical Imaging , vol. 40, no. 12, pp. 3413–3423, 2021
2021
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T. N. N. Doan, B. Song, T. T. Le Vuong, K. Kim, and J. T. Kwak, “Sonnet: A self-guided ordinal regression neural network for segmentation and classification of nuclei in large-scale multi-tissue histology images,” IEEE Journal of Biomedical and Health Informatics , 2022
2022
Later among the works it cites.
T. Ilyas, Z. I. Mannan, A. Khan, S. Azam, H. Kim, and F. De Boer, “Tsfd-net: Tissue specific feature distillation network for nuclei segmentation and classification,” Neural Networks , vol. 151, pp. 1–15, 2022
2022
Later among the works it cites.
T. Hassan, S. Javed, A. Mahmood, T. Qaiser, N. Werghi, and N. Rajpoot, “Nucleus classification in histology images using message passing network,” Medical Image Analysis , p. 102480, 2022
2022
Later among the works it cites.
P. Pati, G. Jaume, A. Foncubierta-Rodríguez, F. Feroce, A. M. Anniciello, G. Scognamiglio, N. Brancati, M. Fiche, E. Dubruc, D. Riccio et al. , “Hierarchical graph representations in digital pathology,” Medical image analysis , vol. 75, p. 102264, 2022
2022
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2022
Later among the works it cites.
B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar, “Masked-attention mask transformer for universal image segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 1290–1299
2022
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S. Graham, Q. D. Vu, M. Jahanifar, S. E. A. Raza, F. Minhas, D. Snead, and N. Rajpoot, “One model is all you need: multi-task learning enables simultaneous histology image segmentation and classification,” Medical Image Analysis , vol. 83, p. 102685, 2023
2023
Closest in time.
S. Chen, C. Ding, M. Liu, J. Cheng, and D. Tao, “Cpp-net: Context-aware polygon proposal network for nucleus segmentation,” IEEE Transactions on Image Processing , 2023
2023
Closest in time.