Deep learning for histopathological image analysis: Towards computerized diagnosis on cancers

Abstract

Automated detection and segmentation of histologic primitives are critical steps for developing computer-aided diagnosis and prognosis system on histopathological tissue specimens. For a number of cancers, the clinical cancer grading system is highly correlated with the pathomic features of histologic primitives that appreciated from histopathological images.However, automated detection and segmentation of histologic primitives is pretty challenged because of the complicity and high density of histologic data. Therefore, there is a high demand for developing intelligent and computational image analysis tools for digital pathology images. Recently there have been interests in the application of “Deep Learning” strategies for classification and analysis of big image data. Histopathology, given its size and complexity, represents an excellent use case for application of deep learning strategies. In this chapter, we present deep learning based approaches for two challenged tasks in histological image analysis: (1) Automated nuclear atypia scoring (NAS) on breast histopathology. We present a Multi-Resolution Convolutional Network (MR-CN) with Plurality Voting (MR-CN-PV) model for automated NAS. MR-CN-PV consists of three Single-Resolution Convolutional Network (SR-CN) with Majority Voting (SR-CN-MV) model for getting independent NAS. MR-CN-PV combines three scores via plurality voting for getting final score. (2) Epithelial (EP) and stromal (ST) tissues discrimination. The work utilized a pixel-wise Convolutional Network (CN-PI) based segmentation model for automated EP and ST tissues discrimination. We present experiments on two challenged datasets. For automated NAS, the MR-CN-PV model was evaluated on MITOS-ATYPIA-14 Challenge dataset. MRCN- PVmodel got 67 scorewhichwas placed the second comparingwith the scores of other five teams. The proposed CN-PI model outperformed patch-wise CN (CN-PA) models in discriminating EP and ST tissues on a breast histological images.