标题： 组织病理学模型训练中的过拟合：定制架构的需求 显示英文标题

标题： Overfitting in Histopathology Model Training: The Need for Customized Architectures

摘要： 本研究调查了应用于组织病理学图像分析的深度学习模型中的过拟合这一关键问题。我们表明，简单采用并微调为自然图像分析设计的大规模模型，通常会导致在应用于组织病理学任务时性能不佳且显著过拟合。通过广泛实验各种模型架构，包括ResNet变体和视觉Transformer（ViT），我们表明增加模型容量并不一定会提高在组织病理学数据集上的性能。我们的发现强调了为组织病理学图像分析专门设计定制架构的需求，特别是在处理有限数据集时。使用食管腺癌公共数据集，我们证明了更简单的、领域特定的架构可以在最小化过拟合的同时实现可比或更好的性能。 显示英文摘要

摘要： This study investigates the critical problem of overfitting in deep learning models applied to histopathology image analysis. We show that simply adopting and fine-tuning large-scale models designed for natural image analysis often leads to suboptimal performance and significant overfitting when applied to histopathology tasks. Through extensive experiments with various model architectures, including ResNet variants and Vision Transformers (ViT), we show that increasing model capacity does not necessarily improve performance on histopathology datasets. Our findings emphasize the need for customized architectures specifically designed for histopathology image analysis, particularly when working with limited datasets. Using Oesophageal Adenocarcinomas public dataset, we demonstrate that simpler, domain-specific architectures can achieve comparable or better performance while minimizing overfitting.