This project aims to develop advanced, interpretable AI-driven techniques for the analysis of state-of-the-art microscopy images, focusing on cancer detection, disease progression prediction, and therapy response evaluation. Leveraging cutting-edge methods such as Graph Neural Networks (GNNs) for analyzing cell interactions, and multimodal imaging techniques, the project seeks to provide deeper insights into the biological processes underpinning cancer. A key objective is to combine information from multiple imaging modalities, including bright-field microscopy of H&E-stained tissues and multiplex immunofluorescence microscopy, to increase the informative content of cancer samples. This multimodal approach enhances the understanding of cancer at the cellular and tissue levels, facilitating early detection and more effective therapeutic interventions. The research will span four years, each focusing on distinct yet interconnected areas. In the first year, we will evaluate and develop novel learning-based methods for analyzing cell distributions and interactions in 2D multiplex images, which will also involve creating a comprehensive multimodal image database. The second year will focus on multimodal analysis and fusion of this data, incorporating additional imaging modalities such as spatial multiomics to further enhance cancer diagnostics and therapy planning. In the third year, efforts will shift to 3D tissue reconstruction and adapting 2D cell interaction analysis methods for 3D environments. The final year will concentrate on developing advanced methods for 3D multimodal analysis of cancer tissues, aiming to improve our understanding of the disease and optimize treatment strategies. By the end of this project, the developed techniques will offer robust, explainable AI models for cancer research, significantly advancing our ability to detect cancer earlier, predict its progression, and plan more effective therapies. The insights gained are expected to contribute to the field of cancer diagnostics and treatment, pushing the boundaries of precision medicine and personalized therapy.