Joint Acne Image Grading and Counting via Label Distribution Learning

Accurate grading of skin disease severity plays a crucial role in precise treatment for patients. Acne vulgaris, the most common skin disease in adolescence, can be graded by evidence-based lesion counting as well as experience-based global estimation in the medical field. However, due to the appearance similarity of acne with close severity, it is challenging to count and grade acne accurately. In this paper, we address the problem of acne image analysis via Label Distribution Learning (LDL) considering the ambiguous information among acne severity. Based on the professional grading criterion, we generate two acne label distributions considering the relationship between the similar number of lesions and severity of acne, respectively. We also propose a unified framework for joint acne image grading and counting, which is optimized by the multi-task learning loss. In addition, we further build the ACNE04 dataset with annotations of acne severity and lesion number of each image for evaluation. Experiments demonstrate that our proposed framework performs favorably against state-of-the-art methods. We make the code and dataset publicly available at https://github.com/xpwu95/ldl.

[1]  Hywel C Williams,et al.  Acne vulgaris , 2012, The Lancet.

[2]  Srinivas S. Kruthiventi,et al.  CrowdNet: A Deep Convolutional Network for Dense Crowd Counting , 2016, ACM Multimedia.

[3]  Zheng Lin,et al.  Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Metin Nafi Gürcan,et al.  Acne image analysis: lesion localization and classification , 2016, SPIE Medical Imaging.

[5]  Jianxin Wu,et al.  Deep Label Distribution Learning With Label Ambiguity , 2016, IEEE Transactions on Image Processing.

[6]  Nuno Vasconcelos,et al.  Counting People With Low-Level Features and Bayesian Regression , 2012, IEEE Transactions on Image Processing.

[7]  Mert R. Sabuncu,et al.  Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[8]  Andrew Zisserman,et al.  Microscopy cell counting and detection with fully convolutional regression networks , 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[9]  Paul L. Rosin,et al.  Clinical Skin Lesion Diagnosis Using Representations Inspired by Dermatologist Criteria , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[10]  B. Dréno,et al.  Epidemiology of Acne , 2003, Dermatology.

[11]  Zhi-Hua Zhou,et al.  Facial Age Estimation by Learning from Label Distributions , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Xin Geng,et al.  Facial Age Estimation by Adaptive Label Distribution Learning , 2014, 2014 22nd International Conference on Pattern Recognition.

[13]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[14]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  Xin Geng,et al.  Head Pose Estimation Based on Multivariate Label Distribution , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Andrew Zisserman,et al.  Learning To Count Objects in Images , 2010, NIPS.

[18]  Makoto Kawashima,et al.  Establishment of grading criteria for acne severity , 2008 .

[19]  D P Krowchuk,et al.  Managing acne in adolescents. , 2000, Pediatric clinics of North America.

[20]  Jufeng Yang,et al.  Joint Image Emotion Classification and Distribution Learning via Deep Convolutional Neural Network , 2017, IJCAI.

[21]  P. Pochi,et al.  The pathogenesis and treatment of acne. , 1990, Annual review of medicine.

[22]  Joost van de Weijer,et al.  Leveraging Unlabeled Data for Crowd Counting by Learning to Rank , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[23]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[24]  Le Zhang,et al.  Historical Context-based Style Classification of Painting Images via Label Distribution Learning , 2018, ACM Multimedia.

[25]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Kouhyar Tavakolian,et al.  Detection and classification of acne lesions in acne patients: A mobile application , 2016, 2016 IEEE International Conference on Electro Information Technology (EIT).

[27]  Bo Ren,et al.  Enhanced-alignment Measure for Binary Foreground Map Evaluation , 2018, IJCAI.

[28]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Jianxin Wu,et al.  Age Estimation Using Expectation of Label Distribution Learning , 2018, IJCAI.

[30]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[31]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[32]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[33]  Daniel Oñoro-Rubio,et al.  Towards Perspective-Free Object Counting with Deep Learning , 2016, ECCV.

[34]  Jufeng Yang,et al.  Learning Visual Sentiment Distributions via Augmented Conditional Probability Neural Network , 2017, AAAI.

[35]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[36]  Xu Yang,et al.  Sparsity Conditional Energy Label Distribution Learning for Age Estimation , 2016, IJCAI.

[37]  Shifeng Zhang,et al.  Single-Shot Refinement Neural Network for Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[38]  Chanjira Sinthanayothin,et al.  Automatic acne detection for medical treatment , 2015, 2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES).

[39]  Xin Geng,et al.  Semi-Supervised Adaptive Label Distribution Learning for Facial Age Estimation , 2017, AAAI.

[40]  Seunghoon Hong,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[41]  Jiangjiang Liu,et al.  Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground , 2018, ECCV.

[42]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[43]  Shenghua Gao,et al.  Single-Image Crowd Counting via Multi-Column Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[44]  Shiv Surya,et al.  Switching Convolutional Neural Network for Crowd Counting , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[45]  Xin Geng,et al.  Hierarchical Classification Based on Label Distribution Learning , 2019, AAAI.

[46]  W J Cunliffe,et al.  Prevalence of facial acne in adults. , 1999, Journal of the American Academy of Dermatology.

[47]  Michele Ermidoro,et al.  Automated detection, extraction and counting of acne lesions for automatic evaluation and tracking of acne severity , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[48]  E. Rückert.  Detecting Pedestrians by Learning Shapelet Features , 2007 .

[49]  Tao Li,et al.  Structure-Measure: A New Way to Evaluate Foreground Maps , 2017, International Journal of Computer Vision.

[50]  Xin Geng,et al.  Soft Facial Landmark Detection by Label Distribution Learning , 2019, AAAI.

[51]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[52]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[53]  Xin Geng,et al.  Crowd counting in public video surveillance by label distribution learning , 2015, Neurocomputing.

[54]  Yang Cao,et al.  Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[55]  Kai Zhao,et al.  Res2Net: A New Multi-Scale Backbone Architecture , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[56]  Fei Su,et al.  Scale Aggregation Network for Accurate and Efficient Crowd Counting , 2018, ECCV.

[57]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[58]  Yueting Zhuang,et al.  Data-Dependent Label Distribution Learning for Age Estimation , 2017, IEEE Transactions on Image Processing.

[59]  Ronald M. Summers,et al.  TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[60]  Xin Geng,et al.  Theoretical Analysis of Label Distribution Learning , 2019, AAAI.

[61]  N. Ranganathan,et al.  Gabor filter-based edge detection , 1992, Pattern Recognit..

[62]  Wenguan Wang,et al.  Shifting More Attention to Video Salient Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[63]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[64]  Kai Wang,et al.  A Benchmark for Automatic Visual Classification of Clinical Skin Disease Images , 2016, ECCV.

[65]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[66]  C. Lawrence Zitnick,et al.  Edge Boxes: Locating Object Proposals from Edges , 2014, ECCV.