Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA estimation using computational techniques can reduce this variability and provide quantitative assessment. Using trichrome-stained whole slide images (WSIs) processed from human renal biopsies, we developed a deep learning framework that captured finer pathological structures at high resolution and overall context at the WSI-level to predict IFTA grade. WSIs (n 67) were obtained from The Ohio State University Wexner Medical Center (OSUWMC).