用迁移学习提升乳腺癌图像分类准确率，ResNet-34表现最佳。
Deep Transfer Learning for Breast Cancer Classification
- 基于VGG、ViT和ResNet进行迁移学习，复用预训练模型知识。
- ResNet-34达到90.40%准确率，VGG-16因参数少获更高F1分数。
- 适合数据少的医学图像分类任务，提升诊断可及性。
乳腺癌是全球影响数百万女性的重大健康问题，早期且准确的分类对治疗和患者预后至关重要。深度迁移学习通过利用预训练模型并在相关任务间转移知识，成为提升乳腺癌分类性能的有力手段。本研究对比分析了VGG、Vision Transformers (ViT) 和 ResNet 在侵袭性导管癌（IDC）图像分类中的表现。结果表明，ResNet-34在分类上表现出显著优势，准确率达90.40%；而预训练的VGG-16因需更新参数较少，获得更高的F1-score。研究认为，深度迁移学习有望极大促进乳腺癌诊断的发展，尤其在数据有限的情况下，使深度学习模型能以少量标注数据高效训练，从而提高筛查的准确性和可及性。
原文摘要 · Abstract (English)
Breast cancer is a major global health issue that affects millions of women worldwide. Classification of breast cancer as early and accurately as possible is crucial for effective treatment and enhanced patient outcomes. Deep transfer learning has emerged as a promising technique for improving breast cancer classification by utilizing pre-trained models and transferring knowledge across related tasks. In this study, we examine the use of a VGG, Vision Transformers (ViT) and Resnet to classify images for Invasive Ductal Carcinoma (IDC) cancer and make a comparative analysis of the algorithms. The result shows a great advantage of Resnet-34 with an accuracy of $90.40\%$ in classifying cancer images. However, the pretrained VGG-16 demonstrates a higher F1-score because there is less parameters to update. We believe that the field of breast cancer diagnosis stands to benefit greatly from the use of deep transfer learning. Transfer learning may assist to increase the accuracy and accessibility of breast cancer screening by allowing deep learning models to be trained with little data.
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