Histopathology diagnosis is an important standard for breast tumors identifying. However, histopathology image analysis is complex, tedious, and error-prone, due to the super-resolution image. In recent years, deep learning technology has been successfully applied to histopathology image analysis and made great progress. The well-known deep neural networks usually have tens of million parameters, which consume much memory to deploy the state-of-the-art model. In addition, deep neural networks rely on high-performance hardware resources, which impede the deployment of the state-of-the-art model on portable equipment. In this work, a novel framework which consists of a weight accumulation method and a lightweight fast neural network (FastNet) was proposed for tumor fast identification (TFI) in mobile-computer-assisted devices. The weight accumulation method was designed to obtain the tissue mask regions of interest and remove the useless background area in histopathology images, which greatly reduces the redundant computation cost. Furthermore, we proposed the lightweight FastNet to improve the computational efficiency on mobile devices. A novel attention loss (AttLoss) function was designed and applied in FastNet. The AttLoss function pays more attention on the positive samples and the indistinguishable samples, which greatly improves the performance. The proposed FastNet was compared with three state-of-the-art methods commonly used for image classification and object detection. Experimental results indicated that FastNet achieves the highest recall of 96.94%, the highest F1F_{1} score of 97.33%, and the highest accuracy of 97.34%, besides least trainable parameters of 0.22M and smallest floating point operations of 210M FLOPs.

中文翻译：

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FastNet：一种轻量级卷积神经网络，用于移动计算机辅助设备中的肿瘤快速识别


组织病理学诊断是乳腺肿瘤鉴别的重要标准。然而，由于超分辨率图像，组织病理学图像分析复杂、繁琐且容易出错。近年来，深度学习技术已成功应用于组织病理学图像分析并取得了长足的进步。众所周知的深度神经网络通常具有数千万个参数，这会消耗大量内存来部署最先进的模型。此外，深度神经网络依赖于高性能硬件资源，这阻碍了最先进模型在便携式设备上的部署。在这项工作中，提出了一种由权重累积方法和轻量级快速神经网络（FastNet）组成的新颖框架，用于移动计算机辅助设备中的肿瘤快速识别（TFI）。设计权重累加方法来获取组织掩模感兴趣区域并去除组织病理学图像中无用的背景区域，大大减少了冗余计算成本。此外，我们提出了轻量级FastNet来提高移动设备上的计算效率。设计了一种新颖的注意力损失（AttLoss）函数并在 FastNet 中应用。 AttLoss函数更加关注正样本和不可区分的样本，大大提高了性能。将所提出的 FastNet 与常用于图像分类和对象检测的三种最先进的方法进行了比较。实验结果表明，FastNet 的召回率最高为 96.94%，F1F_{1} 得分最高为 97.33%，准确率最高为 97.34%，可训练参数最少为 0.22M，浮点运算最少为 210M FLOP。