Deep learning models show remarkable results in automated skin lesion analysis. However, these models demand considerable amounts of data, while the availability of annotated skin lesion images is often limited. Data augmentation can expand the training dataset by transforming input images. In this work, we investigate the impact of 13 data augmentation scenarios for melanoma classification trained on three CNNs (Inception-v4, ResNet, and DenseNet). Scenarios include traditional color and geometric transforms, and more unusual augmentations such as elastic transforms, random erasing and a novel augmentation that mixes different lesions. We also explore the use of data augmentation at test-time and the impact of data augmentation on various dataset sizes. Our results confirm the importance of data augmentation in both training and testing and show that it can lead to more performance gains than obtaining new images. The best scenario results in an AUC of 0.882 for melanoma classification without using external data, outperforming the top-ranked submission (0.874) for the ISIC Challenge 2017, which was trained with additional data.
Some works specifically explore data augmentation for skin lesion analysis @cite_1 @cite_11 @cite_14 . Vasconcelos and Vasconcelos @cite_1 report gains in performance by using data augmentation with geometric transforms (rotations by multiples of 90 degrees; flips; lesion-preserving crops), PCA-based color augmentation, and specialist warping that preserves lesions symmetries and anti-symmetries. @cite_11 highlight the importance of using data augmentation for both training and testing. They averaged the predictions for 50 augmented test samples. @cite_2 compare the effects of data augmentation on classifiers (SVM, neural networks, and random forest) trained with features extracted with a pretrained Inception-v4. Their results indicate that using more samples in test data augmentation (100 vs. 50) increases the model's performance.
@cite_2 @cite_14 @cite_1 @cite_11
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Deep CNN techniques have dramatically become the state of the art in image classification. However, applying high-capacity Deep CNN in medical image analysis has been impeded because of scarcity of labeled data. This study has two primary contributions: first, we propose a classification model to improve performance of classification of skin lesion using Deep CNN and Data Augmentation. Second, we demonstrate the use of image data augmentation for overcoming the problem of data limitation and examine the influence of different number of augmented samples on the performance of different classifiers. The proposed classification system is evaluated using the largest public skin lesion testing dataset, containing 600 testing images, and 6,162 training images. New state-of-the-art performance result is archived with AUC (89.2 vs. 87.4 ), AP (73.9 vs. 71.5 ), and ACC (89.0 vs. 87.2 ). In additional, we explore the influence of each image augmentation on the three classifiers and observe that performance of each classifier is influenced differently by each augmentation and has better results comparing with traditional methods. Thus, it is suggested that the performance of skin cancer classification and medial image classification could be improved further by applying data augmentation. Abstract Skin cancer is a major public health problem, as is the most common type of cancer and represents more than half of cancer diagnosed worldwide. Early detection influences the outcome of the disease and motivates the research presented in this paper. Recent results show that deep learning based approaches learn from data, and can outperform human specialists in a set of tasks when large databases are available for training. This research investigates the scenario where the amount of data available for training is small. It obtains relevant results for the ISBI 2016 melanoma classification challenge (named Skin Lesion Analysis for Melanoma Detection) facing the peculiarities of dealing with such a small and unbalanced biological database. To do this, it explores committees of Deep Convolutional Neural Networks (DCNN), the augmentation of the training data set by image processing classical transforms and by deformations guided by expert knowledge about the lesion axis, and it introduces a third class aiming to improve the classifiers’ distinction of the region of interest of the lesion. The experiments show that the proposed approach improves the final classifier invariance for common melanoma variations, common skin patterns and markers, and dermatoscope capturing conditions. State of the art on melanoma screening evolved rapidly in the last two years, with the adoption of deep learning. Those models, however, pose challenges of their own, as they are expensive to train and difficult to parameterize. Objective: We investigate the methodological issues for designing and evaluating deep learning models for melanoma screening, by exploring nine choices often faced to design deep networks: model architecture, training dataset, image resolution, type of data augmentation, input normalization, use of segmentation, duration of training, additional use of SVM, and test data augmentation. Methods: We perform a two-level full factorial experiment, for five different test datasets, resulting in 2560 exhaustive trials, which we analyze using a multi-way ANOVA. Results: The main finding is that the size of training data has a disproportionate influence, explaining almost half the variation in performance. Of the other factors, test data augmentation and input resolution are the most helpful. Deeper models, when combined, with extra data, also help. We show that the costly full factorial design, or the unreliable sequential optimization, are not the only options: ensembles of models provide reliable results with limited resources. Conclusions and Significance: To move research forward on automated melanoma screening, we need to curate larger shared datasets. Optimizing hyperparameters and measuring performance on the same dataset is common, but leads to overoptimistic results. Ensembles of models are a cost-effective alternative to the expensive full-factorial and to the unstable sequential designs.