@Article{mcb.2018.04292,
AUTHOR = {Ruihan  Zhang, Junhao  Yang, Chunxiao  Chen},
TITLE = {Tumor Cell Identification in Ki-67 Images on Deep Learning},
JOURNAL = {Molecular \& Cellular Biomechanics},
VOLUME = {15},
YEAR = {2018},
NUMBER = {3},
PAGES = {177--187},
URL = {http://www.techscience.com/mcb/v15n3/28621},
ISSN = {1556-5300},
ABSTRACT = {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.},
DOI = {10.3970/mcb.2018.04292}
}