Artificial Intelligence-Based Image Classification for Diagnosis of Skin Cancer: Challenges and Opportunities.

Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasing incidence of skin cancers, low awareness among a growing population, and a lack of adequate clinical expertise and services, there is an immediate need for AI systems to assist clinicians in this domain. A large number of skin lesion datasets are available publicly, and researchers have developed AI-based image classification solutions, particularly deep learning algorithms, to distinguish malignant skin lesions from benign lesions in different image modalities such as dermoscopic, clinical, and histopathology images. Despite the various claims of AI systems achieving higher accuracy than dermatologists in the classification of different skin lesions, these AI systems are still in the very early stages of clinical application in terms of being ready to aid clinicians in the diagnosis of skin cancers. In this review, we discuss advancements in the digital image-based AI solutions for the diagnosis of skin cancer, along with some challenges and future opportunities to improve these AI systems to support dermatologists and enhance their ability to diagnose skin cancer.

[1]  Manu Goyal,et al.  The effect of color constancy algorithms on semantic segmentation of skin lesions , 2019, Medical Imaging.

[2]  Y Q Jiang,et al.  Recognizing basal cell carcinoma on smartphone‐captured digital histopathology images with a deep neural network , 2019, The British journal of dermatology.

[3]  Jorge S. Marques,et al.  Improving Dermoscopy Image Classification Using Color Constancy , 2015, IEEE Journal of Biomedical and Health Informatics.

[4]  Van-Dung Hoang,et al.  Deep CNN and Data Augmentation for Skin Lesion Classification , 2018, ACIIDS.

[5]  Noel C. F. Codella,et al.  Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) , 2016, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[6]  Y. Fujisawa,et al.  Deep‐learning‐based, computer‐aided classifier developed with a small dataset of clinical images surpasses board‐certified dermatologists in skin tumour diagnosis , 2018, The British journal of dermatology.

[7]  Verónica Vilaplana,et al.  BCN20000: Dermoscopic Lesions in the Wild , 2019, Scientific data.

[8]  Paul Babyn,et al.  Generative Adversarial Network in Medical Imaging: A Review , 2018, Medical Image Anal..

[9]  Elizabeth Lazaridou,et al.  Epidemiological trends in skin cancer , 2017, Dermatology practical & conceptual.

[10]  A. Enk,et al.  Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. , 2019, European journal of cancer.

[11]  Vikas J Patel,et al.  Overcalling a teledermatology selfie: a new twist in a growing field. , 2015, Dermatology online journal.

[12]  Manu Goyal,et al.  End-to-end breast ultrasound lesions recognition with a deep learning approach , 2018, Medical Imaging.

[13]  Fabio A. González,et al.  A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection , 2013, MICCAI.

[14]  John Paoli,et al.  Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks , 2019, JAMA dermatology.

[15]  Leslie Ying,et al.  Accelerating magnetic resonance imaging via deep learning , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[16]  Angela Ferrari,et al.  Interactive atlas of dermoscopy , 2000 .

[17]  S. Feldman,et al.  Incidence Estimate of Nonmelanoma Skin Cancer (Keratinocyte Carcinomas) in the U.S. Population, 2012. , 2015, JAMA dermatology.

[18]  A. Jemal,et al.  Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.

[19]  Pedro M. Ferreira,et al.  PH2 - A dermoscopic image database for research and benchmarking , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[20]  M. Binder,et al.  A Prospective Study of Mobile Phones for Dermatology in a Clinical Setting , 2013, Journal of telemedicine and telecare.

[21]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[22]  Tim Holland-Letz,et al.  Pathologist-level classification of histopathological melanoma images with deep neural networks. , 2019, European journal of cancer.

[23]  Renato A. Krohling,et al.  The impact of patient clinical information on automated skin cancer detection , 2020, Comput. Biol. Medicine.

[24]  Saeed Hassanpour,et al.  Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks , 2019, Scientific Reports.

[25]  Woohyung Lim,et al.  Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network , 2018, PloS one.

[26]  S. Han,et al.  Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. , 2018, The Journal of investigative dermatology.

[27]  Achim Hekler,et al.  A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. , 2019, European journal of cancer.

[28]  Andreas K. Maier,et al.  Deep Learning Computed Tomography , 2016, MICCAI.

[29]  Eduardo Valle,et al.  Skin Lesion Synthesis with Generative Adversarial Networks , 2018, OR 2.0/CARE/CLIP/ISIC@MICCAI.

[30]  Cristina Nader Vasconcelos,et al.  Convolutional Neural Network Committees for Melanoma Classification with Classical And Expert Knowledge Based Image Transforms Data Augmentation , 2017, 1702.07025.

[31]  Saeed Hassanpour,et al.  Generative Image Translation for Data Augmentation in Colorectal Histopathology Images , 2019, ML4H@NeurIPS.

[32]  Manu Goyal,et al.  Breast ultrasound lesions recognition: end-to-end deep learning approaches , 2018, Journal of medical imaging.

[33]  Achim Hekler,et al.  Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. , 2019, European journal of cancer.

[34]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[35]  R. Hofmann-Wellenhof,et al.  Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions. , 2020, Annals of oncology : official journal of the European Society for Medical Oncology.

[36]  R. Kirsner,et al.  Comparison of stage at diagnosis of melanoma among Hispanic, black, and white patients in Miami-Dade County, Florida. , 2006, Archives of dermatology.

[37]  Saeed Hassanpour,et al.  Finding a Needle in the Haystack: Attention-Based Classification of High Resolution Microscopy Images , 2018, ArXiv.

[38]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

[39]  Sharath Pankanti,et al.  Deep learning ensembles for melanoma recognition in dermoscopy images , 2016, IBM J. Res. Dev..

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

[41]  Manu Goyal,et al.  Robust Methods for Real-Time Diabetic Foot Ulcer Detection and Localization on Mobile Devices , 2019, IEEE Journal of Biomedical and Health Informatics.

[42]  Manu Goyal,et al.  Region of Interest Detection in Dermoscopic Images for Natural Data-augmentation , 2018, ArXiv.

[43]  P. Harms,et al.  Histologic Mimics of Basal Cell Carcinoma. , 2017, Archives of pathology & laboratory medicine.

[44]  H. Haenssle,et al.  Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.

[45]  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.

[46]  Andrew Y. Ng,et al.  CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning , 2017, ArXiv.

[47]  Ahmed Hosny,et al.  Artificial intelligence in radiology , 2018, Nature Reviews Cancer.

[48]  H. A. M. Daanen,et al.  3D whole body scanners revisited , 2013, Displays.

[49]  Kai Lu,et al.  Interpretable Classification from Skin Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study , 2019, ArXiv.

[50]  R. Hofmann-Wellenhof,et al.  Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. , 2019, The Lancet. Oncology.

[51]  Liron Pantanowitz,et al.  Artificial Intelligence and Digital Pathology: Challenges and Opportunities , 2018, Journal of pathology informatics.

[52]  Anne E Carpenter,et al.  Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.

[53]  Paul L. Rosin,et al.  Self-Paced Balance Learning for Clinical Skin Disease Recognition , 2020, IEEE Transactions on Neural Networks and Learning Systems.

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

[55]  E. Topol,et al.  A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. , 2019, The Lancet. Digital health.

[56]  Hugh M Gloster,et al.  Skin cancer in skin of color. , 2006, Journal of the American Academy of Dermatology.

[57]  Harald Kittler,et al.  Descriptor : The HAM 10000 dataset , a large collection of multi-source dermatoscopic images of common pigmented skin lesions , 2018 .