AnoMalNet：基于离群点检测并利用深度自编码器的疟疾细胞图像分类方法
AnoMalNet: Outlier Detection based Malaria Cell Image Classification Method Leveraging Deep Autoencoder
AI总结：
本文提出 AnoMalNet，一种仅用未感染细胞图像训练自编码器、通过损失阈值进行离群点检测的疟疾细胞分类方法，在极端类别不平衡下取得优于大型模型的性能。
摘要：
类别不平衡是医学图像疾病分类领域中普遍存在的问题。为了获得良好的结果，在训练模型时需要平衡类别分布。然而，对于罕见医学疾病，来自患病患者的图像比来自未患病患者的图像更难获取，从而导致不希望出现的类别不平衡。迄今为止，人们已经探索了多种处理类别不平衡问题的方法，每种方法都有其各自的缺点。在本研究中，我们提出了一种基于离群点检测的二分类医学图像分类技术，该技术甚至能够处理最极端的类别不平衡情况。我们使用了一个包含疟疾寄生细胞和未感染细胞的数据集。一个名为 AnoMalNet 的自编码器模型最初仅使用未感染细胞图像进行训练，然后通过对损失值进行阈值化来对患病细胞和未患病细胞图像进行分类。我们分别取得了 98.49% 的准确率、97.07% 的精确率、100% 的召回率和 98.52% 的 F1 分数，表现优于大型深度学习模型和其他已发表的工作。由于我们提出的方法无需在训练阶段使用疾病阳性样本即可提供具有竞争力的结果，因此它应能在不平衡数据集上的二分类疾病分类中发挥作用。
英文摘要：
Class imbalance is a pervasive issue in the field of disease classification from medical images. It is necessary to balance out the class distribution while training a model for decent results. However, in the case of rare medical diseases, images from affected patients are much harder to come by compared to images from non-affected patients, resulting in unwanted class imbalance. Various processes of tackling class imbalance issues have been explored so far, each having its fair share of drawbacks. In this research, we propose an outlier detection based binary medical image classification technique which can handle even the most extreme case of class imbalance. We have utilized a dataset of malaria parasitized and uninfected cells. An autoencoder model titled AnoMalNet is trained with only the uninfected cell images at the beginning and then used to classify both the affected and non-affected cell images by thresholding a loss value. We have achieved an accuracy, precision, recall, and F1 score of 98.49%, 97.07%, 100%, and 98.52% respectively, performing better than large deep learning models and other published works. As our proposed approach can provide competitive results without needing the disease-positive samples during training, it should prove to be useful in binary disease classification on imbalanced datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
