Background and Objective

Malignant melanoma (MM) is one of the deadliest types of skin cancer. Analysing dermatoscopic images plays an important role in the early detection of MM and other pigmented skin lesions. Among different computer-based methods, deep learning-based approaches and in particular convolutional neural networks have shown excellent classification and segmentation performances for dermatoscopic skin lesion images. These models can be trained end-to-end without requiring any hand-crafted features. However, the effect of using lesion segmentation information on classification performance has remained an open question.

Methods

In this study, we explicitly investigated the impact of using skin lesion segmentation masks on the performance of dermatoscopic image classification. To do this, first, we developed a baseline classifier as the reference model without using any segmentation masks. Then, we used either manually or automatically created segmentation masks in both training and test phases in different scenarios and investigated the classification performances. The different scenarios included approaches that exploited the segmentation masks either for cropping of skin lesion images or removing the surrounding background or using the segmentation masks as an additional input channel for model training.

Results

Evaluated on the ISIC 2017 challenge dataset which contained two binary classification tasks (i.e. MM vs. all and seborrheic keratosis (SK) vs. all) and based on the derived area under the receiver operating characteristic curve scores, we observed four main outcomes. Our results show that 1) using segmentation masks did not significantly improve the MM classification performance in any scenario, 2) in one of the scenarios (using segmentation masks for dilated cropping), SK classification performance was significantly improved, 3) removing all background information by the segmentation masks significantly degraded the overall classification performance, and 4) in case of using the appropriate scenario (using segmentation for dilated cropping), there is no significant difference of using manually or automatically created segmentation masks.

Conclusions

We systematically explored the effects of using image segmentation on the performance of dermatoscopic skin lesion classification.

中文翻译：

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皮肤病变分割对皮肤镜图像分类性能的影响。

背景与目的

恶性黑色素瘤（MM）是最致命的皮肤癌类型之一。分析皮肤镜图像在MM和其他色素沉着的皮肤病变的早期检测中起着重要作用。在不同的基于计算机的方法中，基于深度学习的方法，尤其是卷积神经网络，对于皮肤镜皮肤病变图像显示出出色的分类和分割性能。这些模型可以端到端地训练，而无需任何手工特征。但是，使用病变分割信息对分类性能的影响仍然是一个悬而未决的问题。

方法

在这项研究中，我们明确调查了使用皮肤病变分割蒙版对皮肤镜图像分类性能的影响。为此，首先，我们开发了一个基线分类器作为参考模型，而没有使用任何分割蒙版。然后，我们在不同场景的训练和测试阶段都使用了手动或自动创建的分割蒙版，并研究了分类性能。不同的场景包括利用分割蒙版裁剪皮肤病变图像或去除周围背景或将分割蒙版用作模型训练的附加输入通道的方法。

结果

在ISIC 2017挑战数据集上进行了评估，该数据集包含两个二元分类任务（即MM vs.所有和脂溢性角化病（SK）vs. all），并根据受试者工作特征曲线评分下的推导面积，我们观察到四个主要结果。我们的结果表明：1）在任何情况下使用分割蒙版都不会显着改善MM分类性能； 2）在其中一种情况下（使用分割蒙版进行膨胀裁剪），SK分类性能得到了显着改善； 3）删除了所有背景信息分割蒙版大大降低了整体分类性能，并且4）在使用适当方案的情况下（使用分割进行放大裁剪），使用手动或自动创建的分割蒙版没有显着差异。

结论

我们系统地探讨了使用图像分割对皮肤镜皮肤病变分类性能的影响。