Intratumor heterogeneity (ITH) presents challenges for precision oncology, but methods for its spatial quantification, scalable at population levels, do not exist. Based on previous work showing that admixture of PAM50 subtype can be measured from bulk tissue using transcriptomic data, we trained a deep neural network (DNN) to quantify subtype ITH in Luminal A (LumA) breast cancer from routinely-stained whole slide images. We tested the hypothesis that subtype admixture detected in images was associated with tumor aggressiveness and adverse outcome. In 680 cases from the TCGA-BRCA cohort, we determined adherence to assigned subtype by applying matrix factorization to each transcriptome. The purest cases were split into groups for initial testing, training and parameter tuning. 230 LumA-assigned cases were held out for final testing. Image patches were fed into a DNN pre-trained on histology images. We measured the association of tumor area classified as LumA in the image to tumor characteristics and survival. Among LumA-assigned cases, admixture was associated with slightly higher ER-positivity but lower PR-positivity and ER-related gene expression, and higher HER2-positivity, tumor size, grade, and TNM stage. Image admixture was associated with more TP53 and less PIK3CA mutation. Progression-free survival was significantly shorter in more admixed cases. Our findings demonstrate that deep learning, trained to recognize genomic correlates in tissue morphology, can quantify and map subtype admixture in LumA breast cancer that has clinical significance. The low-cost and scalability of this method holds potential as a research tool for investigating ITH and perhaps improving the efficacy of precision oncology.