Yang Ziyuan, Leng Lu, Li Ming, Chu Jun
School of Software, Nanchang Hangkong University, Nanchang, 330063 People's Republic of China.
College of Computer Science, Sichuan University, Chengdu, 610065 People's Republic of China.
Multimed Tools Appl. 2022;81(11):15671-15686. doi: 10.1007/s11042-022-12565-0. Epub 2022 Feb 28.
The abnormal traits and colors of feces typically indicate that the patients are probably suffering from tumor or digestive-system diseases. Thus a fast, accurate and automatic health diagnosis system based on feces is urgently necessary for improving the examination speed and reducing the infection risk. The rarity of the pathological images would deteriorate the accuracy performance of the trained models. In order to alleviate this problem, we employ augmentation and over-sampling to expand the samples of the classes that have few samples in the training batch. In order to achieve an impressive recognition performance and leverage the latent correlation between the traits and colors of feces pathological samples, a multi-task network is developed to recognize colors and traits of the macroscopic feces images. The parameter number of a single multi-task network is generally much smaller than the total parameter number of multiple single-task networks, so the storage cost is reduced. The loss function of the multi-task network is the weighted sum of the losses of the two tasks. In this paper, the weights of the tasks are determined according to their difficulty levels that are measured by the fitted linear functions. The sufficient experiments confirm that the proposed method can yield higher accuracies, and the efficiency is also improved.
粪便的异常特征和颜色通常表明患者可能患有肿瘤或消化系统疾病。因此，迫切需要一个基于粪便的快速、准确且自动的健康诊断系统，以提高检查速度并降低感染风险。病理图像的稀缺会降低训练模型的准确性。为了缓解这个问题，我们采用增强和过采样来扩充训练批次中样本较少的类别的样本。为了实现令人印象深刻的识别性能并利用粪便病理样本的特征和颜色之间的潜在相关性，开发了一个多任务网络来识别宏观粪便图像的颜色和特征。单个多任务网络的参数数量通常远小于多个单任务网络的总参数数量，因此降低了存储成本。多任务网络的损失函数是两个任务损失的加权和。在本文中，任务的权重根据由拟合线性函数测量的难度级别来确定。充分的实验证实，所提出的方法可以产生更高的准确率，并且效率也得到了提高。
