Objective interpretation of myocardial ^99mTc-pyrophosphate (PYP) uptake is important in suspected cardiac amyloidosis. We developed a segmentation-informed multimodal framework that classifies myocardial PYP uptake from planar and single-photon emission computed tomography (SPECT) images. The retrospective, class-enriched cohort comprised 49 studies from 44 patients (19 PYP-positive and 25 PYP-negative) with specialist-confirmed image labels. Five consecutive SPECT slices were manually selected under nuclear-medicine-physician supervision; subsequent processing was automated. SPECT Net generated an unthresholded myocardial probability map, which served as a soft, spatially interpretable representation for integration with planar features. Patient-grouped five-fold cross-validation used separate validation and test folds. In PYP-positive patients, cropped nnU-Net achieved a patient-level Dice coefficient of $0.9209\pm 0.0316$ and outperformed three segmentation comparators after Holm correction ($p<0.001$). Transformer-style fusion achieved 95.5% accuracy (95% confidence interval: 84.9–98.7%), 89.5% sensitivity, 100.0% specificity, and a 94.4% F1-score. Improvements over planar-only classification (90.9%; McNemar $p=0.500$), localized raw SPECT (86.4%; $p=0.125$), and H/CL assessment (88.6%; $p=0.250$) were not significant. These single-center internal results demonstrate the feasibility of using a soft myocardial segmentation output as an interpretable bridge for multimodal PYP uptake classification but do not establish superiority; multicenter external validation is required.