SpringNet: A Novel Deep Neural Network Architecture for Histopathological Image Analysis

Reliable and capable tools are needed for segmentation and classification of nuclei in histopathology images to improve the workflow of histopathologists and lessen the burden of diagnosis. Analyzing histopathology images can be challenging for deep learning models due to the various sources of noise. We propose a pipeline for segmenting and classifying nuclei, trained on a combination of datasets to ensure robustness. This makes our pipeline capable of generalizing on image tiles with varying Hematoxylin & Eosin concentrations. For nuclei classification, we present a novel convolutional neural network classifier that demonstrate state-of-the-art classification performance on the Lizard dataset.