IEEE Trans Biomed Eng. 2020 Feb;67(2):495-503. doi: 10.1109/TBME.2019.2915839. Epub 2019 May 9.
This paper addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification. The second problem is the high-class imbalance encountered in real-world multi-class datasets.
To use high-resolution images, we propose a novel patch-based attention architecture that provides global context between small, high-resolution patches. We modify three pretrained architectures and study the performance of patch-based attention. To counter class imbalance problems, we compare oversampling, balanced batch sampling, and class-specific loss weighting. Additionally, we propose a novel diagnosis-guided loss weighting method that takes the method used for ground-truth annotation into account.
Our patch-based attention mechanism outperforms previous methods and improves the mean sensitivity by [Formula: see text]. Class balancing significantly improves the mean sensitivity and we show that our diagnosis-guided loss weighting method improves the mean sensitivity by [Formula: see text] over normal loss balancing.
The novel patch-based attention mechanism can be integrated into pretrained architectures and provides global context between local patches while outperforming other patch-based methods. Hence, pretrained architectures can be readily used with high-resolution images without downsampling. The new diagnosis-guided loss weighting method outperforms other methods and allows for effective training when facing class imbalance.
The proposed methods improve automatic skin lesion classification. They can be extended to other clinical applications where high-resolution image data and class imbalance are relevant.
本文解决了皮肤病变分类的两个关键问题。第一个问题是如何有效利用经过预训练的标准架构的高分辨率图像进行图像分类。第二个问题是在实际的多类别数据集遇到的高类别不平衡问题。
为了使用高分辨率图像，我们提出了一种新的基于补丁的注意力架构，它提供了小的高分辨率补丁之间的全局上下文。我们修改了三个预训练的架构，并研究了基于补丁的注意力的性能。为了应对类别不平衡问题，我们比较了过采样、平衡批次采样和特定类别损失加权。此外，我们提出了一种新的基于诊断的损失加权方法，该方法考虑了用于真实标签注释的方法。
我们的基于补丁的注意力机制优于以前的方法，平均敏感性提高了[Formula: see text]。类别平衡显著提高了平均敏感性，我们表明我们的基于诊断的损失加权方法比正常的损失平衡方法提高了[Formula: see text]的平均敏感性。
新的基于补丁的注意力机制可以集成到预训练的架构中，在局部补丁之间提供全局上下文，同时优于其他基于补丁的方法。因此，无需下采样即可直接使用预训练的架构处理高分辨率图像。新的基于诊断的损失加权方法优于其他方法，并且在面对类别不平衡时可以有效地进行训练。
所提出的方法提高了皮肤病变的自动分类。它们可以扩展到其他涉及高分辨率图像数据和类别不平衡的临床应用中。
