Department of Electrical and Information Engineering, University of Cassino and L.M., Via G. Di Biasio 43, 03043 Cassino (FR), Italy.
Department of Electrical, Information Engineering and Applied Mathematics, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano (SA), Italy.
Artif Intell Med. 2020 Mar;103:101749. doi: 10.1016/j.artmed.2019.101749. Epub 2019 Nov 13.
In this paper, we propose a novel method for the detection of small lesions in digital medical images. Our approach is based on a multi-context ensemble of convolutional neural networks (CNNs), aiming at learning different levels of image spatial context and improving detection performance. The main innovation behind the proposed method is the use of multiple-depth CNNs, individually trained on image patches of different dimensions and then combined together. In this way, the final ensemble is able to find and locate abnormalities on the images by exploiting both the local features and the surrounding context of a lesion. Experiments were focused on two well-known medical detection problems that have been recently faced with CNNs: microcalcification detection on full-field digital mammograms and microaneurysm detection on ocular fundus images. To this end, we used two publicly available datasets, INbreast and E-ophtha. Statistically significantly better detection performance were obtained by the proposed ensemble with respect to other approaches in the literature, demonstrating its effectiveness in the detection of small abnormalities.
在本文中，我们提出了一种用于检测数字医学图像中小病灶的新方法。我们的方法基于多上下文卷积神经网络（CNN）的集成，旨在学习图像空间上下文的不同层次，提高检测性能。所提出方法的主要创新之处在于使用多个深度 CNN，分别对不同尺寸的图像块进行训练，然后将它们组合在一起。通过这种方式，最终的集成能够通过利用病灶的局部特征和周围上下文来发现和定位图像上的异常。实验集中在两个最近用 CNN 解决的著名医学检测问题上：全数字化乳腺 X 线摄影中的微钙化检测和眼底图像中的微动脉瘤检测。为此，我们使用了两个公开可用的数据集 INbreast 和 E-ophtha。与文献中的其他方法相比，所提出的集成方法在检测小病灶方面表现出了更好的性能，证明了其在检测小异常方面的有效性。
