Automatic Breast Cancer Grading of Histological Images using Dilated Residual Network
Author:
Affiliation:
1. Department of Computer Science and Technology, Tsinghua University, Beijing, China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3340074.3340077
Reference14 articles.
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2. Andreou D Werner M Pink D etal Prognostic relevance of the mitotic count and the amount of viable tumour after neoadjuvant chemotherapy for primary localised high-grade soft tissue sarcoma. Br J Cancer. 2014;112(3):455--60. Andreou D Werner M Pink D et al. Prognostic relevance of the mitotic count and the amount of viable tumour after neoadjuvant chemotherapy for primary localised high-grade soft tissue sarcoma. Br J Cancer. 2014;112(3):455--60.
3. Alex Skovsbo Jørgensen Rasmussen A M Niels Kristian Mäkinen Andersen et al. Using cell nuclei features to detect colon cancer tissue in hematoxylin and eosin stained slides{J}. Cytometry Part A 91(8) 2017. Alex Skovsbo Jørgensen Rasmussen A M Niels Kristian Mäkinen Andersen et al. Using cell nuclei features to detect colon cancer tissue in hematoxylin and eosin stained slides{J}. Cytometry Part A 91(8) 2017.
4. Classification of Breast Cancer Histology Image using Ensemble of Pre-trained Neural Networks
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1. Application of Deep Learning in Histopathology Images of Breast Cancer: A Review;Micromachines;2022-12-11
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