Quantitative Dermatopathology of 11 Skin Conditions through Transfer Learning of Tissue Photon Interaction Statistical Physics

Quantitative dermatopathology is being widely employed in clinical practices for precise detection of various skin conditions. Dermatoscopes are placed in contact with the skin for visual observation and this (a) risks cross-contamination between investigated subjects and (b) limits instrument use when investigating infectious lesions and wounds. This paper presents a framework for quantitative dermatopathology using consumer grade camera for contact free imaging of skin conditions. Inductive transfer learning of tissue-photon-interaction (TPI) statistical physics modeled for 11 skin conditions estimates TPI as a spatially localized poisson process of photons sensed by the RGB sensors, and utilizes this knowledge to detect skin condition. Inter-camera and illumination variations are compensated by including a maximum likelihood estimation (MLE) based photon density normalization across all images in the dataset. 8-folded crossvalidation experiments over a dataset of 440 images of 11 skin conditions provided the condition identification accuracy of 94.55 ± 5.05% within the top-3 predictions.