PhDHistopathology imaging is a type of microscopy imaging commonly used for the microlevel clinical examination of a patient’s pathology. Due to the extremely large size of histopathology images, especially whole slide images (WSIs), it is difficult for pathologists to make a quantitative assessment by inspecting the details of a WSI. Hence, a computeraided system is necessary to provide a subjective and consistent assessment of the WSI for personalised treatment decisions. In this thesis, a deep learning framework for the automatic analysis of whole slide histopathology images is presented for the first time, which aims to address the challenging task of assessing and grading colorectal liver metastasis (CRLM). Quantitative evaluations of a patient’s condition with CRLM are conducted through quantifying different tissue components in resected tumorous specimens. This study mimics the visual examination process of human experts, by focusing on three levels of information, the tissue level, cell level and pixel level, to achieve the step by step segmentation of histopathology images. At the tissue level, patches with category information are utilised to analyse the WSIs. Both classification-based approaches and segmentation-based approaches are investigated to locate the metastasis region and quantify different components of the WSI. For the classification-based method, different factors that might affect the classification accuracy are explored using state-of-the-art deep convolutional neural networks (DCNNs). Furthermore, a novel network is proposed to merge the information from different magnification levels to include contextual information to support the final decision. With the support by the segmentation-based method, edge information from the image is integrated with the proposed fully convolutional neural network to further enhance the segmentation results. At the cell level, nuclei related information is examined to tackle the challenge of inadequate annotations. The problem is approached from two aspects: a weakly supervised nuclei detection and classification method is presented to model the nuclei in the CRLM by integrating a traditional image processing method and variational auto-encoder (VAE). A novel nuclei instance segmentation framework is proposed to boost the accuracy of the nuclei detection and segmentation using the idea of transfer learning. Afterwards, a fusion framework is proposed to enhance the tissue level segmentation results by leveraging the statistical and spatial properties of the cells. At the pixel level, the segmentation problem is tackled by introducing the information from the immunohistochemistry (IHC) stained images. Firstly, two data augmentation approaches, synthesis-based and transfer-based, are proposed to address the problem of insufficient pixel level segmentation. Afterwards, with the paired image and masks having been obtained, an end-to-end model is trained to achieve pixel level segmentation. Secondly, another novel weakly supervised approach based on the generative adversarial network (GAN) is proposed to explore the feasibility of transforming unpaired haematoxylin and eosin (HE) images to IHC stained images. Extensive experiments reveal that the virtually stained images can also be used for pixel level segmentation
