Acne, a prevalent skin condition, necessitates precise severity assessment for effective treatment. Acne severity grading typically involves lesion counting and global assessment. However, manual grading suffers from variability and inefficiency, highlighting the need for automated tools. Recently, label distribution learning (LDL) was proposed as an effective framework for acne image grading, but its effectiveness is hindered by severity scales that assign varying numbers of lesions to different severity grades. Addressing these limitations, we proposed to incorporate severity scale information into lesion counting by combining LDL with label smoothing, and to decouple if from global assessment. A novel weighting scheme in our approach adjusts the degree of label smoothing based on the severity grading scale. This method helped to effectively manage label uncertainty without compromising class distinctiveness. Applied to the benchmark ACNE04 dataset, our model demonstrated improved performance in automated acne grading, showcasing its potential in enhancing acne diagnostics. The source code is publicly available at http://github.com/openface-io/acne-lds.
Acne Vulgaris seriously affects people’s daily life. In this paper, we propose a face acne grading framework which is a new paradigm to solve the image classification problem where the number and type of small objects are the evidence. This framework includes two components: prior knowledge extraction and prior knowledge guided network. The prior knowledge extraction uses an excellent segmentation method to predict the lesion areas as prior knowledge. The prior knowledge guided network fuses the prior knowledge and its corresponding image to grade the severity. The experiment results demonstrate that our framework achieves the state-of-the-art and diagnosis level of dermatologists.
Severity level grading is a vitally important step to make correct diagnoses and personalized treatment schemes for acne, which is mainly carried out in two ways: criterion-based lesion counting and experience-based global estimation. In this paper, the global estimation of acne severity grading is studied by Convolutional Neural Networks (CNNs) and a unified acne grading framework that can diagnose referring to different grading criteria is proposed. Firstly, an adaptive image preprocessing method that can efficiently reduce the background noise and emphasize the skin information is proposed. Next, an innovative CNN structure SFNet, which fuses local skin features with global features to effectively enhance the perception of color gaps between skin and lesion, is presented. The proposed framework is verified on two datasets with different acne grading criteria. Experimental results show that the accuracy of the proposed framework reaches 84.52% exceeding the state-of-the-art method by 1.7% and reaches the diagnostic level of a professional dermatologist.
Acne seriously affects people’s daily life. Acne severity level grading plays a decisive role in the cure. However, the acne criterion is not unified in the medical field. Most of the current studies explore the application of advanced visual models on acne severity grading but lack the adaptation to the characteristics of acne diagnosis. Meanwhile, some studies propose specifically adapted methods for respectively used acne criteria but can not be used on other acne criteria. In this study, we propose an acne diagnosis method, Diagnostic Evidence Distillation (DED), that suitably adapts the characteristics of acne diagnosis and can be applied to diagnose under different acne criteria. Firstly, we fully investigate and analyze the commonality of various acne criteria and condense the face acne diagnosis into an unconventional image classification problem based on the diagnostic evidence of type and number of fine-grained lesions distributed on a whole face. Next, we propose the DED framework to adapt the characteristics of acne diagnosis. This framework uses the teacher-student structure of knowledge distillation methods to bring the diagnostic evidence, which is not available for new patients but only in training data, into the diagnosis model. To break the limitation of different criteria, the framework uses convolutional neural networks (CNNs) as backbones to imitate the global estimation of dermatology. We also propose a subtask joint learning for the teacher network to enhance its guidance to the student. The DED is applied to diagnose acne on two datasets ACNE04 and PLSBRACNE01 based on different mainstream acne criteria. The experimental results demonstrate that on both datasets, the DED effectively improves the diagnosis performance, exceeds the state-of-the-art and reaches the diagnostic level of dermatologists. The precision, sensitivity, specificity, Youden Index and accuracy reach 85.31%, 84.83%, 94.66%, 79.48% and 86.06% on the ACNE04 dataset and 69.16%, 65.62%, 88.93%, 54.54% and 67.56% on the PLSBRACNE01 dataset, respectively.
Grading the severity level is an extremely important procedure for correct diagnoses and personalized treatment schemes for acne. However, the acne grading criteria are not unified in the medical field. This work aims to develop an acne diagnosis system that can be generalized to various criteria. Methods: A unified acne grading framework that can be generalized to apply referring to different grading criteria is developed. It imitates the global estimation of the dermatologist diagnosis in two steps. First, an adaptive image preprocessing method effectively filters meaningless information and enhances key information. Next, an innovative network structure fuses global deep features with local features to simulate the dermatologists’ comparison of local skin and global observation. In addition, a transfer fine-tuning strategy is proposed to transfer prior knowledge on one criterion to another criterion, which effectively improves the framework performance in case of insufficient data. Results: The Preprocessing method effectively filters meaningless areas and improves the performance of downstream models.The framework reaches accuracies of 84.52% and 59.35% on two datasets separately. Conclusions: The application of the framework on acne grading exceeds the state-of-the-art method by 1.71%, reaches the diagnostic level of a professional dermatologist and the transfer fine-tuning strategy improves the accuracy of 6.5% on the small data.