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Accurate diagnosis of breast cancer in histopathology images is challenging due to the heterogeneity of cancer cell growth as well as of a variety of benign breast tissue proliferative lesions. In this work, we propose a practical and self interpretable invasive cancer diagnosis solution. With minimum annotation information, the proposed method mines contrast patterns between normal and malignant images in unsupervised manner and generates a probability map of abnormalities to verify its reasoning. Particularly, a fully convolutional autoencoder is used to learn the dominant structural patterns among normal image patches. Patches that do not share the characteristics of this normal population are detected and analyzed by one-class support vector machine and 1-layer neural network. We apply the proposed method to a public breast cancer image set. Ourresults, in consultation with a senior pathologist, demonstrate that the proposed method outperforms existing methods. The obtained probability map could benefit the pathology practice by providing visualized verification data and potentially leads to a better understanding of data-driven diagnosis solutions.
About the author
Prof.Gajanan Kale is Presently working as Assistant Professor in CSE Dept. at FTC COER,Sangola and he is having more than 10 years of Academic experience and handling various subjects for UG-PG level. Dr.Somnath Thigale is Head & Associate Professor at CSE Dept. FTC COER,Sangla & having 17+ years of experience.
