Virtual Pathology in Automated Diagnosis of Skin Cancer: Feature Learning and Mapping Is Almost All You Need

The automated diagnosis of skin cancer is a very challenging task. Aiming to imitate dermatologists, computer aided diagnostic (CAD) of skin cancer based on artificial intelligence (AI) provide performance close or superior to dermatologist with clinical images acquired from smartphones taking into account also the patient lesion information. Although the addition of patient's lesion information as if the lesion bleeds, or hurts, or if it itches, among others, do improve the diagnosis metrics, they are not enough to correctly discriminate the main skin cancer lesions. The gold-standard in clinical diagnosis is the biopsy exam. For that, data was collected from 2018 in partnership with the university hospital at UFES, resulting in more than 2000 clinical images of the main skin lesions taken by smarpthones with the corresponding patient lesion information. The main value of the dataset is that it is certified with the histopathological exams (gold-standard). The dataset PAD-UFES-20 is one of the real-world dataset used in this area since 2020. Now, we propose a new methodology to use the information of the histopathological images to automated diagnosis. Our approach to solve the problem uses a convolutional neural network (CNN) to extract features of the clinical images and a second one to extract features of the corresponding histopathological images and a feedforward neural network for feature mapping, which learns to map the extracted clinical features in its corresponding histopathological features. Next, the entire framework for training the model firstly extracts features of the clinical images and use the previously trained neural network for feature mapping to provide the corresponding histopathological features. Both features (extracted and mapped) are fused and inputted to a neural network classifier providing the final diagnosis.