Ductal Carcinoma In Situ (DCIS) is a non-obligatory precursor of Invasive Breast Cancer. It is the most common mammographically detected breast cancer. Predicting DCIS progression to invasive ductal carcinoma is a major clinical challenge due to the lack of a uniform classification system in the diagnosis and prognostication of this disease. To characterise the tissue microecology of DCIS, we proposed and tested the model "DCIS-Identification model"based on Generative Adversarial Networks (GAN) for detection and segmentation of DCIS ducts from multiplex immunohistochemistry (IHC) staining samples. We also trained a Spatially Constrained Convolutional Neural Network (SC-CNN) to detect and classify single cells based on their CA9 and FOXP3 expression. The DCIS-Identification model was evaluated on 8 whole slide images, resulting in an average Dice score of 0.95 for the segmentation performance. The single cell identification framework was tested on 10 randomly selected whole slide sections, achieving the average accuracy of 88.6% in a 5 fold cross validation scheme. With the proposed pipeline, we efficiently integrated deep learning, computational pathology and spatial statistics to report distinct differences in the microenvironments of DCIS and IDC/DCIS samples. The proposed pipeline provides a tool for a better understanding of the mechanism of tumours in DCIS and IDC/DCIS cases.