We present a multi-scale graphical network that can capture the relevant representations of individual cell morphology, topological structure of cell communities in a tissue image, as well as whole slide level attributes. This helps to effectively merge the disease relevant cell morphology to the overall topological context within the sample, within one unified deep framework. From the explainability point of view, instead of empirical design, the graphs are designed with biomedical considerations in mind in order to have translational validity. We also provide a clinically interpretable visualisation of the cells and their micro- and macro-environment by leveraging label noise reduction. We demonstrate the efficacy of our methodology on myeloproliferative neoplasms (MPN), a haematopoietic stem cell disorder as an exemplar test case. The proposed method achieves an encouraging performance in the robust separation of different MPN subtypes in this exciting new dataset as part of this work.