| Cancer diagnosis from tissue biomarker scoring is a vital technique used in determining type and grade of cancer. This is a significant part of workload for pathologists, the process is tedious, time consuming, subjective, error prone and lacks inter-pathologist agreement. Thousands of patients are misdiagnosed each year, and several automated image analysis techniques using Deep Neural Networks (DNN) have been proposed for analyzing histopathology images for various cancer types and datasets. Typical challenges for a deep neural network to operate in this setting are limited datasets, gigapixel images and small percentage and high variability of nuclei indicative of malignant tumors. Previous approaches have focused on applying DNNs to different cancer imaging datasets, but their theoretical understanding of the problem is limited. In this work, we aim to gain fundamental insights into the nature of problem and propose a single model which can diagnose several types of cancers. Further, we employ recent advances in one-shot learning to enable our model to learn and expand to different types of cancer only from a few examples. We demonstrate good performance of our model on cervical cancer dataset. |
