Modic changes are common degenerative lesions seen in spinal MRI and are strongly linked to lower back pain. However, detection of Modic changes suffers from poor inter-operator and inter-scanner reliabilities. We present a fully automatic, quantitative model that leverages deep learning and signal-based clustering for mapping Modic changes from clinically acquired MRI. The model achieves an identification rate of 85.7% and substantial agreement with radiologists. More importantly, the mapping technique classifies detected lesions on a voxel-wise basis, allowing for assessment of sensitive, local pathologies.