Mobile Dermatoscopy: Class Imbalance Management Based on Blurring Augmentation, Iterative Refining and Cost-Weighted Recall Loss

Q3 Computer Science
Nauman Ullah Gilal, Samah Ahmed Mustapha Ahmed, J. Schneider, Mowafa J Househ, Marco Agus
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引用次数: 0

Abstract

We present an end-to-end framework for real-time melanoma detection on mole images acquired with mobile devices equipped with off-the-shelf magnifying lens. We trained our models by using transfer learning through EfficientNet convolutional neural networks by using public domain The International Skin Imaging Collaboration (ISIC)-2019 and ISIC-2020 datasets. To reduce the class imbalance issue, we integrated the standard training pipeline with schemes for effective data balance using oversampling and iterative cleaning through loss ranking. We also introduce a blurring scheme able to emulate the aberrations produced by commonly available magnifying lenses, and a novel loss function incorporating the difference in cost between false positive (melanoma misses) and false negative (benignant misses) predictions. Through preliminary experiments, we show that our framework is able to create models for real-time mobile inference with controlled tradeoff between false positive rate and false negative rate. The obtained performances on ISIC-2020 dataset are the following: accuracy 96.9%, balanced accuracy 98%, ROCAUC=0.98, benign recall 97.7%, malignant recall 97.2%.
移动皮肤镜:基于模糊增强、迭代精炼和成本加权召回损失的类不平衡管理
我们提出了一个端到端的框架,实时黑色素瘤检测的痣图像与配备现成的放大镜的移动设备获得。我们通过使用公共领域的国际皮肤成像协作(ISIC)-2019和ISIC-2020数据集,通过高效网络卷积神经网络使用迁移学习来训练我们的模型。为了减少类不平衡问题,我们将标准训练管道与使用过采样和通过损失排序进行迭代清理的有效数据平衡方案集成在一起。我们还引入了一种能够模拟常用放大镜产生的像差的模糊方案,以及一种新的损失函数,该函数结合了假阳性(黑色素瘤遗漏)和假阴性(良性遗漏)预测之间的成本差异。通过初步实验,我们表明我们的框架能够创建实时移动推理模型,并在假阳性率和假阴性率之间进行可控权衡。在ISIC-2020数据集上获得的性能如下:准确率96.9%,平衡准确率98%,ROCAUC=0.98,良性召回率97.7%,恶性召回率97.2%。
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来源期刊
中国图象图形学报
中国图象图形学报 Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.20
自引率
0.00%
发文量
6776
期刊介绍: Journal of Image and Graphics (ISSN 1006-8961, CN 11-3758/TB, CODEN ZTTXFZ) is an authoritative academic journal supervised by the Chinese Academy of Sciences and co-sponsored by the Institute of Space and Astronautical Information Innovation of the Chinese Academy of Sciences (ISIAS), the Chinese Society of Image and Graphics (CSIG), and the Beijing Institute of Applied Physics and Computational Mathematics (BIAPM). The journal integrates high-tech theories, technical methods and industrialisation of applied research results in computer image graphics, and mainly publishes innovative and high-level scientific research papers on basic and applied research in image graphics science and its closely related fields. The form of papers includes reviews, technical reports, project progress, academic news, new technology reviews, new product introduction and industrialisation research. The content covers a wide range of fields such as image analysis and recognition, image understanding and computer vision, computer graphics, virtual reality and augmented reality, system simulation, animation, etc., and theme columns are opened according to the research hotspots and cutting-edge topics. Journal of Image and Graphics reaches a wide range of readers, including scientific and technical personnel, enterprise supervisors, and postgraduates and college students of colleges and universities engaged in the fields of national defence, military, aviation, aerospace, communications, electronics, automotive, agriculture, meteorology, environmental protection, remote sensing, mapping, oil field, construction, transportation, finance, telecommunications, education, medical care, film and television, and art. Journal of Image and Graphics is included in many important domestic and international scientific literature database systems, including EBSCO database in the United States, JST database in Japan, Scopus database in the Netherlands, China Science and Technology Thesis Statistics and Analysis (Annual Research Report), China Science Citation Database (CSCD), China Academic Journal Network Publishing Database (CAJD), and China Academic Journal Network Publishing Database (CAJD). China Science Citation Database (CSCD), China Academic Journals Network Publishing Database (CAJD), China Academic Journal Abstracts, Chinese Science Abstracts (Series A), China Electronic Science Abstracts, Chinese Core Journals Abstracts, Chinese Academic Journals on CD-ROM, and China Academic Journals Comprehensive Evaluation Database.
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