Our research on automated screening for melanoma was accepted for SIBGRAPI’2014, the Brazilian conference on Graphics, Patterns and Images, to be held in Rio de Janeiro next month.
Melanoma is the most dangerous skin cancer type, being responsible for the majority of deaths due to skin diseases. It is, on the other hand, one of the most curable forms of cancer when it is detected early enough. Because the prevalence of melanoma is increasing throughout the world, tools for the automated screening — a test for wether or not the patient should seek a dermatologist — are a public health necessity. Automated screening is particularly important in poor, rural, or isolated communities, with no resident dermatologist.
The paper, “Statistical Learning Approach for Robust Melanoma Screening”, advances the state of the art by employing a cutting-edge extension to the bags-of-words model called BossaNova. Here’s the abstract :
According to the American Cancer Society, one person dies of melanoma every 57 minutes, although it is the most curable type of cancer if detected early. Thus, computer-aided diagnosis for melanoma screening has been a topic of active research. Much of the existing art is based on the Bag-of-Visual-Words (BoVW) model, combined with color and texture descriptors. However, recent advances in the BoVW model, as well as the evaluation of the importance of the many different factors affecting the BoVW model were yet to be explored, thus motivating our work. We show that a new approach for melanoma screening, based upon the state-of-the-art BossaNova descriptors, shows very promising results for screening, reaching an AUC of up to 93.7%. An important contribution of this work is an evaluation of the factors that affect the performance of the two-layered BoVW model. Our results show that the low-level layer has a major impact on the accuracy of the model, but that the codebook size on the mid-level layer is also important. Those results may guide future works on melanoma screening.
In addition, Michel Fornaciali has created a mini-site with extra information about the paper, including the executables for the method we implemented, and the AUC measure of all 320 runs employed in the statistical analysis.
The dataset employed was kindly provided by the researchers of German project IRMA, hosted at the RWTH Aachen University. We are working with them in order to make all the data publicly available.