基于卷积神经网络的焦点损失用于类别不平衡问题：犬红细胞形态分类的案例研究
Convolutional Neural Networks based Focal Loss for Class Imbalance Problem: A Case Study of Canine Red Blood Cells Morphology Classification
AI总结：
本文针对犬红细胞形态分类中的类别不平衡问题，提出将卷积神经网络与焦点损失函数相结合的方法，并通过双CNN和5折交叉验证证明其相比交叉熵损失能有效提升F1分数、减少对多数类的偏向。
摘要：
红细胞形态通常由病理学家进行判读，这一过程耗时且费力。此外，红细胞形态的误分类会导致错误的疾病诊断和不恰当的治疗。因此，一名合格的病理学家必须真正成为红细胞形态分类方面的专家。在过去十年中，已有许多方法被提出用于人类红细胞形态分类。然而，这些方法尚未解决分类中的类别不平衡问题。类别不平衡问题——即各类别中样本数量差异很大的问题——是可能导致模型偏向多数类的问题之一。由于每种异常血细胞形态都很罕见，采集过程得到的数据通常是不平衡的。在本研究中，我们旨在通过使用卷积神经网络（CNN）——一种众所周知的深度学习技术——并结合擅长处理类别不平衡问题的焦点损失函数（focal loss function），专门解决犬红细胞形态分类中的这一问题。所提出的技术在一个精心设计的框架上进行：使用两种不同的CNN来验证焦点损失函数的有效性，并通过5折交叉验证确定最优超参数。实验结果表明，与使用不解决类别不平衡问题的传统交叉熵损失函数增强的模型相比，使用焦点损失函数增强的两个CNN模型均取得了更高的$F_{1}$分数。换言之，在不平衡犬红细胞数据的分类任务中，焦点损失函数确实使CNN模型比交叉熵更少地偏向多数类。
英文摘要：
Morphologies of red blood cells are normally interpreted by a pathologist. It is time-consuming and laborious. Furthermore, a misclassified red blood cell morphology will lead to false disease diagnosis and improper treatment. Thus, a decent pathologist must truly be an expert in classifying red blood cell morphology. In the past decade, many approaches have been proposed for classifying human red blood cell morphology. However, those approaches have not addressed the class imbalance problem in classification. A class imbalance problem---a problem where the numbers of samples in classes are very different---is one of the problems that can lead to a biased model towards the majority class. Due to the rarity of every type of abnormal blood cell morphology, the data from the collection process are usually imbalanced. In this study, we aimed to solve this problem specifically for classification of dog red blood cell morphology by using a Convolutional Neural Network (CNN)---a well-known deep learning technique---in conjunction with a focal loss function, adept at handling class imbalance problem. The proposed technique was conducted on a well-designed framework: two different CNNs were used to verify the effectiveness of the focal loss function and the optimal hyper-parameters were determined by 5-fold cross-validation. The experimental results show that both CNNs models augmented with the focal loss function achieved higher $F_{1}$-scores, compared to the models augmented with a conventional cross-entropy loss function that does not address class imbalance problem. In other words, the focal loss function truly enabled the CNNs models to be less biased towards the majority class than the cross-entropy did in the classification task of imbalanced dog red blood cell data.
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。
