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Introduction: Acne is one of the most common pathologies and affects people of all ages, genders, and ethnicities. The assessment of the type and severity status of a patient with acne should be done by a dermatologist, but the ever-increasing waiting time for an examination makes the therapy not accessible as quickly and consequently less effective. This work, born from the collaboration with CHOLLEY, a Swiss company with decades of experience in the research and production of skin care products, with the aim of developing a deep learning system that, using images produced with a mobile device, could make assessments and be as effective as a dermatologist. Methods: There are two main challenges within this task. The first is to have enough data to train a neural model. Unlike other works in the literature, it was decided not to collect a proprietary dataset, but rather to exploit the enormity of public data available in the world of face analysis. Part of Flickr-Faces-HQ (FFHQ) was re-annotated by a CHOLLEY dermatologist, producing a dataset that is sufficiently large, but still very extendable. The second challenge was to simultaneously use high-resolution images to provide the neural network with the best data quality, but at the same time to ensure that the network learned the task correctly. To prevent the network from searching for recognition patterns in some uninteresting regions of the image, a semantic segmentation model was trained to distinguish, what is a skin region possibly affected by acne and what is background and can be discarded. Results: Filtering the re-annotated dataset through the semantic segmentation model, the trained classification model achieved a final average f1 score of 60.84% in distinguishing between acne affected and unaffected faces, result that, if compared to other techniques proposed in the literature, can be considered as state-of-the-art.
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BACKGROUND: Current alignment tools typically lack an explicit model of indel evolution, leading to artificially short inferred alignments (i.e., over-alignment) due to inconsistencies between the indel history and the phylogeny relating the input sequences. RESULTS: We present a new progressive multiple sequence alignment tool ProPIP. The process of insertions and deletions is described using an explicit evolutionary model-the Poisson Indel Process or PIP. The method is based on dynamic programming and is implemented in a frequentist framework. The source code can be compiled on Linux, macOS and Microsoft Windows platforms. The algorithm is implemented in C++ as standalone program. The source code is freely available on GitHub at https://github.com/acg-team/ProPIP and is distributed under the terms of the GNU GPL v3 license. CONCLUSIONS: The use of an explicit indel evolution model allows to avoid over-alignment, to infer gaps in a phylogenetically consistent way and to make inferences about the rates of insertions and deletions. Instead of the arbitrary gap penalties, the parameters used by ProPIP are the insertion and deletion rates, which have biological interpretation and are contextualized in a probabilistic environment. As a result, indel rate settings may be optimised in order to infer phylogenetically meaningful gap patterns.
