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.

翻译：曼氏血吸虫病是一种在70多个国家流行的寄生虫病，其诊断通常通过显微镜下对粪便样本中的寄生虫虫卵进行视觉计数来完成。最先进的目标检测算法基于重型神经网络，不适合在实验室常规诊断中实现自动化。我们通过提出一种轻量级卷积神经网络来解决这一问题，其权重仅为最先进目标检测器的数千分之一。该方法中的卷积核通过用户绘制的涂鸦在极少量训练图像上标注的注意力区域逐层学习。通过视觉识别和选择代表性卷积核，在降低计算成本的同时提升性能。另一创新在于单层自适应解码器，其卷积权重可根据每幅图像实时自动定义。实验表明，根据五项评估指标，我们的CNN可超越三种最先进基线方法，且适合实验室常规CPU环境运行，每个可用线程每秒可处理约四幅图像。