Schistosomiasis mansoni is an endemic parasitic disease in more than seventy countries, whose diagnosis is commonly performed by visually counting the parasite eggs in microscopy images of fecal samples. State-of-the-art (SOTA) object detection algorithms are based on heavyweight neural networks, unsuitable for automating the diagnosis in the laboratory routine. We circumvent the problem by presenting a flyweight Convolutional Neural Network (CNN) that weighs thousands of times less than SOTA object detectors. The kernels in our approach are learned layer-by-layer from attention regions indicated by user-drawn scribbles on very few training images. Representative kernels are visually identified and selected to improve performance with reduced computational cost. Another innovation is a single-layer adaptive decoder whose convolutional weights are automatically defined for each image on-the-fly. The experiments show that our CNN can outperform three SOTA baselines according to five measures, being also suitable for CPU execution in the laboratory routine, processing approximately four images a second for each available thread.

该研究利用轻量级卷积神经网络进行血吸虫病诊断，该模型层次学习关注区域并选择代表性卷积，通过自动定义卷积权重适应各种图像，实验表明该模型性能高于三种常规模型，可适用于实验室常规检测，每秒处理约四张图像。

一种轻量级卷积神经网络及自适应解码器用于血吸虫卵检测