DIOR-ViT: 用于病理图像癌症分类的差分序数学习视觉Transformer
DIOR-ViT: Differential Ordinal Learning Vision Transformer for Cancer Classification in Pathology Images
AI总结：
针对计算病理学中癌症分级未利用等级排序特性的问题，提出DIOR-ViT差分序数学习视觉Transformer，结合定制损失函数同时实现类别与差分序数分类，在三类癌症数据集上提升了分级准确率与可靠性。
摘要：
在计算病理学领域，癌症分级主要被作为类别分类问题开展研究，未利用癌症分级的排序特性（如分级越高，癌症病情越严重）。为纳入癌症分级间的排序关系，本文提出差分序数学习问题，通过样本对在特征空间中的差异，定义并学习样本对类别标签间的差异程度。为此，本文提出一种基于Transformer的神经网络，可同时执行癌症分级的类别分类和差分序数分类任务，还为差分序数学习设计了定制化损失函数。在三类不同癌症数据集上评估所提方法，结果表明采用差分序数学习可提升癌症分级的准确性与可靠性，性能优于传统癌症分级方法。该方法可推广应用于其他存在类别标签间序数关系的疾病与问题。
英文摘要：
In computational pathology, cancer grading has been mainly studied as a categorical classification problem, which does not utilize the ordering nature of cancer grades such as the higher the grade is, the worse the cancer is. To incorporate the ordering relationship among cancer grades, we introduce a differential ordinal learning problem in which we define and learn the degree of difference in the categorical class labels between pairs of samples by using their differences in the feature space. To this end, we propose a transformer-based neural network that simultaneously conducts both categorical classification and differential ordinal classification for cancer grading. We also propose a tailored loss function for differential ordinal learning. Evaluating the proposed method on three different types of cancer datasets, we demonstrate that the adoption of differential ordinal learning can improve the accuracy and reliability of cancer grading, outperforming conventional cancer grading approaches. The proposed approach should be applicable to other diseases and problems as they involve ordinal relationship among class labels.
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。
