Automated Detection and Categorization of Genital Injuries Using Digital Colposcopy

摘要

Despite the existence of patterns able to discriminate between consensual and non-consensual intercourse, the relevance of genital lesions in the corroboration of a legal rape complaint is currently under debate in many countries. The testimony of the physicians when assessing these lesions has been questioned in court due to several factors (e.g. a lack of comprehensive knowledge of lesions, wide spectrum of background area, among others). Thereby, it is relevant to provide automated tools to support the decision process in an objective manner. In this work, we compare traditional handcrafted features and deep learning techniques in the automated processing of colposcopic images for genital injury detection. Positive results where achieved by both paradigms in segmentation and classification subtasks, being traditional and deep models the best strategy for each subtask type respectively.