Melanoma is diagnosed in approximately 124,000 people and is responsible for about 10,000 deaths every year, in the USA. Dermatologists rely on visual and dermatoscopic examination to discriminate benign melanocytic lesions from malignant, resulting in high and highly variable benign-to-malignant biopsy ratios from 8:1 to 47:1, and millions of unnecessary biopsies of benign lesions. Reflectance confocal microscopy (RCM) imaging has been proven for noninvasively guiding diagnosis of melanoma in several large clinical studies. RCM imaging at the dermal-epidermal junction (DEJ) provides sensitivity of 92-88% and specificity of 71-84%. The specificity is 2 times superior to that of dermatoscopy. RCM imaging at the DEJ is now being implemented to rule out malignancy, reduce biopsy and guide treatment. However, this is currently at only a few sites, where there are highly trained experts who can ensure that imaging is appropriately performed and images are read correctly. These experts are a small international cohort of “early adopter” clinicians, who have worked with RCM technology during the past decade and have become highly skilled readers. For novice (non-expert) clinicians in the wider cohort who are keen to adopt RCM, learning to read images is challenging and requires substantial effort and time. Two major technical barriers underlie the dramatic variability in diagnostic accuracy among novice clinicians. Together they limit utility, reproducibility and wider adoption of RCM. The first is user dependent subjective variability in depths near the DEJ at which images are acquired, and the second is variability in interpretation of images. We propose to address these barriers with computational “multi-faceted” classification modeling (innovation), image analysis and machine learning algorithms. Our specific aims are: (1) to develop and evaluate algorithms for both dermatoscopic images and RCM depth-stacks, to enable automated standardized and consistent acquisition of RCM mosaics at the DEJ in melanocytic lesions; (2) to develop and evaluate algorithms to discriminate patterns of cellular morphology at the DEJ into two classes, benign lesions versus malignant (dysplastic lesions and melanoma); and (3) to test our algorithms on patients for acquisition of RCM mosaics and classification into those two groups, with statistical validation against pathology, with statistical validation against pathology. Preliminary studies show that our algorithms can delineate the DEJ with accuracy in the range ~3-13 μm in strongly pigmented dark skin and ~5-20 μm in lightly pigmented fair skin, and can detect cellular morphologic patterns with sensitivity in the range 67-80% and specificity 78-99%. Melanocytic lesions can be distinguished from the surrounding normal skin at the DEJ with 80% classification accuracy. Our success will produce standardized imaging and analysis approaches, to advance RCM for noninvasive detection of melanoma. Furthermore, these approaches can be useful for non-melanoma skin cancers, cutaneous lymphoma and other skin disorders (wider impact).