Preprint Essay Version 1 This version is not peer-reviewed

Multi-Scale Deep Neural Network for Mitosis Detection in Breast Cancer Histological Images

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. Preprints 2017, 2017110063 (doi: 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 (doi: 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.

Subject Areas

breast cancer; mitosis detection; CNN; Stain-normalization; CRF; multi-scale feature

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