Application of deep learning to segmentation and grading of fine needle biopsy breast images

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The effectiveness of cancer treatment largely depends on the early detection of the disease. Important and often used diagnostic methods are x rays, cat scans, MRI scans and ultrasonography imaging. Once early signs of cancer are detected, doctors often recommend biopsy of suspicious mass. For many types of cancers, including breast cancer and liver cancer, fine needle aspiration biopsy (FNB) is a preferred diagnostic technique as it is less invasive than the open and closed surgical biopsy. Determining cancer grade from FNB images may be a challenging task even for pathologists since aspiration process may result in partial destruction of the tissue structure and sometimes even of nuclei. Recent advances in machine learning and high performance data computation have created new possibilities for intelligent processing of medical images with high accuracy. In particular, deep convolutional neural networks have demonstrated the capacity to recognize various objects within an image, and can outperform state-of-the-art object recognition techniques. The proposed research aims at creating an innovative computational system for rapid and reliable cancer grading utilizing computer-expert imaging software developed by the ORS company for automatic segmentation and grading of virtual FNB slides using deep convolutional neural networks combined with the state-of-the-art random forest classifiers.