Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA.
Skin Res Technol. 2012 Nov;18(4):389-96. doi: 10.1111/j.1600-0846.2011.00584.x. Epub 2011 Dec 5.
Telangiectasia, tiny skin vessels, are important dermoscopy structures used to discriminate basal cell carcinoma (BCC) from benign skin lesions. This research builds off of previously developed image analysis techniques to identify vessels automatically to discriminate benign lesions from BCCs.
A biologically inspired reinforcement learning approach is investigated in an adaptive critic design framework to apply action-dependent heuristic dynamic programming (ADHDP) for discrimination based on computed features using different skin lesion contrast variations to promote the discrimination process. Lesion discrimination results for ADHDP are compared with multilayer perception backpropagation artificial neural networks.
This study uses a data set of 498 dermoscopy skin lesion images of 263 BCCs and 226 competitive benign images as the input sets. This data set is extended from previous research [Cheng et al., Skin Research and Technology, 2011, 17: 278]. Experimental results yielded a diagnostic accuracy as high as 84.6% using the ADHDP approach, providing an 8.03% improvement over a standard multilayer perception method.
We have chosen BCC detection rather than vessel detection as the endpoint. Although vessel detection is inherently easier, BCC detection has potential direct clinical applications. Small BCCs are detectable early by dermoscopy and potentially detectable by the automated methods described in this research.
微静脉，即微小的皮肤血管，是用于区分基底细胞癌（BCC）与良性皮肤病变的重要皮肤镜结构。本研究基于先前开发的图像分析技术，旨在自动识别血管，以区分良性病变与 BCC。
在自适应评论家设计框架中研究了一种受生物启发的强化学习方法，应用基于计算特征的动作相关启发式动态规划（ADHDP）进行判别，使用不同的皮肤病变对比度变化来促进判别过程。将 ADHDP 的病变判别结果与多层感知机反向传播人工神经网络进行比较。
本研究使用了 498 个皮肤镜皮肤病变图像的数据集，其中包括 263 个 BCC 和 226 个竞争良性图像作为输入集。该数据集是从先前的研究[Cheng 等人，皮肤研究与技术，2011，17:278]中扩展而来。实验结果表明，使用 ADHDP 方法的诊断准确率高达 84.6%，比标准的多层感知机方法提高了 8.03%。
我们选择 BCC 检测而不是血管检测作为终点。虽然血管检测本身更容易，但 BCC 检测具有潜在的直接临床应用。通过皮肤镜检查可以早期检测到小的 BCC，并且可能可以通过本研究中描述的自动方法进行检测。
