Recent advances in multiplexed tissue imaging, such as multiplexed immunohistochemistry (mIHC) and Mass Cytometry Imaging (MCI), together with the traditional brightfield tissue whole slide imageing, enable the capturing of a large amount of tissue phenotypic and molecular data at a cellular and sub-cellular resolution. The Lens Platform aims to provide a suite of software tools for multi-modal tissue imaging data analysis for cancer research, empowered with deep learning based methods. Specially, the following tools are developed: 1) Registration: A set of serial WSI registration software to enable the integration of multi-stained serial brightfield WSIs and multiplexed tissue biomarker imaging at the full image resolution; 2) Pathology structure segmentation: A set of deep learning models for biomarker detection and histology object segmentation; 3) Spatial analytics: A suite of efficient and scalable spatial analytics tools for spatial TME analytics; Preditive Analytics: 4) Deep learning models for cancer prediction analysis with multi-modal tissue imaging data and processed spatial TME features; and Web Portal: 5) A web-based software platform to integrate all software components to support multi-modal image data management, visualization, annotation, analysis and dissemination.