Version 1
: Received: 9 November 2017 / Approved: 9 November 2017 / Online: 9 November 2017 (10:05:53 CET)
How to cite:
Wu, B.; Fan, B.; Xiao, Q.; Kausar, T.; Wang, W. Multi-Scale Deep Neural Network for Mitosis Detection in Breast Cancer Histological Images. Preprints2017, 2017110063. https://doi.org/10.20944/preprints201711.0063.v1
Wu, B.; Fan, B.; Xiao, Q.; Kausar, T.; Wang, W. Multi-Scale Deep Neural Network for Mitosis Detection in Breast Cancer Histological Images. Preprints 2017, 2017110063. https://doi.org/10.20944/preprints201711.0063.v1
Wu, B.; Fan, B.; Xiao, Q.; Kausar, T.; Wang, W. Multi-Scale Deep Neural Network for Mitosis Detection in Breast Cancer Histological Images. Preprints2017, 2017110063. https://doi.org/10.20944/preprints201711.0063.v1
APA Style
Wu, B., Fan, B., Xiao, Q., Kausar, T., & Wang, W. (2017). Multi-Scale Deep Neural Network for Mitosis Detection in Breast Cancer Histological Images. Preprints. https://doi.org/10.20944/preprints201711.0063.v1
Chicago/Turabian Style
Wu, B., Tasleem Kausar and Wenfeng Wang. 2017 "Multi-Scale Deep Neural Network for Mitosis Detection in Breast Cancer Histological Images" Preprints. https://doi.org/10.20944/preprints201711.0063.v1
Abstract
Accurate assessment of the breast cancer deterioration degree plays a crucial role in making medical plan, and the important basis for degree assessment is the number of mitoses in a given area of the pathological image. We utilized deep multi-scale fused fully convolutional neural network (MFF-CNN) combing with conditional random felid (CRF) to detect mitoses in hematoxylin and eosin stained histology image. Analyze the characteristics of mitotic detection ----scale invariance and sparsity, as well as the difficulties ---- small amount of data , inconsistent image staining and sample class unbalanced. Based on this, mitotic detection model is designed. In this paper, a tissue-based staining equalization method is used, and to establish an effective training sample set, we select training samples by using CNN. A mitotic detection model fusing multi-level and multi-scale features and context information was designed, and the corresponding training strategy was made to reduce over-fitting. As preliminarily validated on the public 2014 ICPR MITOSIS data, our method achieves a better performance in term of detection accuracy than ever recorded for this dataset.
Keywords
breast cancer; mitosis detection; CNN; Stain-normalization; CRF; multi-scale feature
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
