Recent state-of-the-art methods for skin lesion segmentation are based on convolutional neural networks (CNNs). Even though these CNN-based segmentation approaches are accurate, they are computationally expensive. In this paper, we address this problem and propose an efficient fully convolutional neural network, named DermoNet. In DermoNet, due to our densely connected convolutional blocks and skip connections, network layers can reuse information from their preceding layers and ensure high accuracy in later network layers. By doing so, we take advantage of the capability of high-level feature representations learned at intermediate layers with varying scales and resolutions for lesion segmentation. Quantitative evaluation is conducted on three well-established public benchmark datasets: the ISBI 2016, ISBI 2017, and the PH2 datasets. The experimental results show that our proposed approach outperforms the state-of-the-art algorithms on these three datasets. We also compared the runtime performance of DermoNet with two other related architectures, which are fully convolutional networks and U-Net. The proposed approach is found to be faster and suitable for practical applications.

中文翻译：

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DermoNet：紧密链接的卷积神经网络，用于有效的皮肤病变分割

皮肤病变分割的最新技术是基于卷积神经网络（CNN）的。即使这些基于CNN的分割方法准确无误，但在计算上却很昂贵。在本文中，我们解决了这个问题，并提出了一个有效的全卷积神经网络，称为DermoNet。在DermoNet中，由于我们紧密连接的卷积块和跳过连接，网络层可以重用其先前层中的信息，并确保后续网络层中的高精度。通过这样做，我们利用了在中间层学习的高级特征表示的能力，这些特征具有用于病变分割的不同尺度和分辨率。在三个公认的公共基准数据集上进行定量评估：ISBI 2016，ISBI 2017和PH2数据集。实验结果表明，我们提出的方法在这三个数据集上均优于最新算法。我们还将DermoNet的运行时性能与其他两个相关的体系结构进行了比较，它们是完全卷积网络和U-Net。发现所提出的方法更快并且适合于实际应用。