Abstract
Being a non-histone protein, Ki-67 is one of the essential biomarkers for the immunohistochemical assessment of proliferation rate in breast cancer screening and grading. The Ki-67 signature is always sensitive to radiotherapy and chemotherapy. Due to random morphological, color and intensity variations of cell nuclei (immunopositive and immunonegative), manual/subjective assessment of Ki-67 scoring is error-prone and time-consuming. Hence, several machine learning approaches have been reported; nevertheless, none of them had worked on deep learning based hotspots detection and proliferation scoring. In this article, we suggest an advanced deep learning model for computerized recognition of candidate hotspots and subsequent proliferation rate scoring by quantifying Ki-67 appearance in breast cancer immunohistochemical images. Unlike existing Ki-67 scoring techniques, our methodology uses Gamma mixture model (GMM) with Expectation-Maximization for seed point detection and patch selection and deep learning, comprises with decision layer, for hotspots detection and proliferation scoring. Experimental results provide 93% precision, 0.88% recall and 0.91% F-score value. The model performance has also been compared with the pathologists’ manual annotations and recently published articles. In future, the proposed deep learning framework will be highly reliable and beneficial to the junior and senior pathologists for fast and efficient Ki-67 scoring.
Highlights
Identification of accurate grading remains always a challenge for pathologists
Due to the heterogeneous and massive dataset in medical imaging or biomedical applications scientists are getting interested in deep learning
This paper has been structured as an introduction, literature review, experimental setup, results & discussion and conclusion
Summary
Identification of accurate grading (grade I, grade II and grade III) remains always a challenge for pathologists. In the rural and urban areas with minimum or few advanced instrumentation, manual inspection of Ki-67 scoring may provide wrong results. Automated assessment of Ki-67 scoring is highly required. The automatic scoring will provide high throughput, more objective and reproducible results in comparison with the manual evaluation. The proliferation score is calculated as the ratio between total numbers of immunopositive nuclei and a total number of nuclei present in the image[11]. The Ki-67 automated assessment was done mainly based on conventional imaging techniques. Due to the heterogeneous and massive dataset in medical imaging or biomedical applications scientists are getting interested in deep learning. Deep learning is a versatile biomedical research tool with numerous potential applications. Y. Xu et al (2014) reported deep learning for medical image analysis[13]. This paper has been structured as an introduction, literature review, experimental setup, results & discussion and conclusion
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