TY - EJOU AU - Zhang, Ruihan AU - Yang, Junhao AU - Chen, Chunxiao TI - Tumor Cell Identification in Ki-67 Images on Deep Learning T2 - Molecular \& Cellular Biomechanics PY - 2018 VL - 15 IS - 3 SN - 1556-5300 AB - The proportion of cells staining for the nuclear antigen Ki-67 is an important predictive indicator for assessment of tumor cell proliferation and growth in routine pathological investigation. Instead of traditional scoring methods based on the experience of a trained laboratory scientist, deep learning approach can be automatically used to analyze the expression of Ki-67 as well. Deep learning based on convolutional neural networks (CNN) for image classification and single shot multibox detector (SSD) for object detection are used to investigate the expression of Ki-67 for assessment of biopsies from patients with breast cancer in this study. The results focus on estimating the probability heatmap of tumor cells using CNN with accuracy of 98% and detecting the tumor cells using SSD with accuracy of 90%. This deep learning framework will provide an objective basis for the malignant degree of breast tumors and be beneficial to the pathologists for fast and efficiently Ki-67 scoring. KW - Ki-67 KW - breast cancer KW - convolution neural networks KW - single shot multibox detector DO - 10.3970/mcb.2018.04292