Abstract
Cancer is one of the leading health challenges for women, specifically breast and ovarian cancer. Early detection can help improve the survival rate through timely intervention and treatment. Traditional methods of detecting cancer involve manually examining mammograms, CT scans, ultrasounds, and other imaging types. However, this makes the process labor-intensive and requires the expertise of trained pathologists. Hence, making it both time-consuming and resource-intensive. In this paper, we introduce a novel vision transformer (ViT)-based method for detecting and classifying breast and ovarian cancer. We use a pre-trained ViT-Base-Patch16-224 model, which is fine-tuned for both binary and multi-class classification tasks using publicly available histopathological image datasets. Further, we use a preprocessing pipeline that converts raw histophological images into standardized PyTorch tensors, which are compatible with the ViT architecture and also help improve the model performance. We evaluated the performance of our model on two benchmark datasets: the BreakHis dataset for binary classification and the UBC-OCEAN dataset for five-class classification without any data augmentation. Our model surpasses existing CNN, ViT, and topological data analysis-based approaches in binary classification. For multi-class classification, it is evaluated against recent topological methods and demonstrates superior performance. Our study highlights the effectiveness of Vision Transformer-based transfer learning combined with efficient preprocessing in oncological diagnostics.
Abstract (translated)
癌症是女性面临的重大健康挑战之一，特别是乳腺癌和卵巢癌。早期检测有助于通过及时的干预和治疗来提高生存率。传统上，癌症检测方法包括手动检查乳房X线摄影（钼靶）、CT扫描、超声波等影像资料。然而，这使得过程变得繁琐，并且需要有经验的专业病理学家进行解读，从而耗时且耗费资源。 在本文中，我们提出了一种基于视觉变换器（Vision Transformer, ViT）的新方法来检测和分类乳腺癌与卵巢癌。我们使用了一个预训练的ViT-Base-Patch16-224模型，并针对二元分类和多类分类任务，在公开的历史病理图像数据集上进行了微调。此外，我们还设计了一套预处理管道，将原始历史病理图片转化为标准化的PyTorch张量，这些张量与ViT架构兼容并有助于提升模型性能。 我们在两个基准数据集上评估了我们的模型的表现：BreakHis数据集用于二元分类任务，UBC-OCEAN数据集用于五类无数据增强情况下的多类别分类。在二元分类中，我们的模型超越了现有的卷积神经网络（CNN）、ViT和基于拓扑数据分析的方法；而在多类分类方面，它与最新的拓扑方法进行了对比，并展示了更为优越的性能。 本研究强调了视觉变换器基础的迁移学习与高效的预处理结合在肿瘤诊断中的有效性。
URL
https://arxiv.org/abs/2509.18553
