bibtext: @InProceedings{ Gaz_AcneAI_MICCAI2024,
                   author = { Gazeau, Léa and Nguyen, Hang and Nguyen, Zung and Lebedeva, Mariia and Nguyen, Thanh and To, Tat-Dat and Le Digabel, Jimmy and Filiol, Jérome and Josse, Gwendal and Perlis, Clifford and Wolfe, Jonathan },
                   title = { { AcneAI: A new acne severity assessment method using digital images and deep learning } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: In this paper we present a new AcneAI system that automatically analyses facial acne images in a precise way, detecting and scoring every single acne lesion within an image. Its workflow consists of three main steps: 1) segmentation of all acne and acne-like lesions, 2) scoring of each acne lesion, 3) combining individual acne lesion scores into an overall acne severity score for the whole image, that ranges from 0 to 100. Our clinical tests on the Acne04 dataset shows that AcneAI has an Intraclass Correlation Coefficient (ICC) score of 0.8 in severity classification. We obtained an area under the curve (AUC) of 0.88 in detecting inflammatory lesions in a clinical dataset obtained from a multi-centric clinical trial.
title: AcneAI: A new acne severity assessment method using digital images and deep learning
authors: Gazeau, Léa Nguyen, Hang Nguyen, Zung Lebedeva, Mariia Nguyen, Thanh To, Tat-Dat Le Digabel, Jimmy Filiol, Jérome Josse, Gwendal Perlis, Clifford Wolfe, Jonathan