Most cancers and neuro-related diseases (e.g., autism and stroke) display significant phenotypic and genetic heterogeneity. Characterizing such heterogeneity could transform our understanding of the etiology of these conditions and inspire new approaches to urgently needed preventions, diagnoses, and treatments. However, existing statistical methods face major challenges in delineating such heterogeneity at both group and individual levels. The aim of this paper is to propose a novel statistical disease mapping (SDM) framework to address some of these challenges. We develop an efficient estimation method to estimate unknown parameters in SDM and individual and group disease maps. Both simulation studies and real data analysis on the ADNI PET dataset indicate that our SDM can not only effectively detect diseased regions in each patient, but also provide a group disease map analysis of Alzheimer (AD) subgroups.