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的小病变的新方法。我们的方法基于卷积神经网络（CNN）的多上下文合奏，旨在学习不同级别的图像空间上下文并提高检测性能。所提出方法背后的主要创新是使用多深度CNN，这些CNN在不同尺寸的图像块上进行单独训练，然后组合在一起。通过这种方式，最终合奏能够通过利用病变的局部特征和周围环境来发现和定位图像上的异常。实验的重点是CNN最近面临的两个著名的医学检测问题：全场数字X线照片的微钙化检测和眼底图像的微动脉瘤检测。为此，我们使用了两个公开可用的数据集：INbreast和E-ophtha。相对于文献中的其他方法，所提出的集合获得了统计学上显着更好的检测性能，证明了其在检测小异常中的有效性。