组织病理学模型训练中的过拟合：定制架构的必要性
Overfitting in Histopathology Model Training: The Need for Customized Architectures
AI总结：
本研究针对组织病理学图像分析中深度学习模型的过拟合问题，通过实验证明定制化简单架构比大规模通用模型更有效，能减少过拟合并提升性能。
摘要：
本研究探讨了深度学习模型在组织病理学图像分析中应用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，在应用于组织病理学任务时，往往会导致次优性能和显著的过拟合。通过使用包括ResNet变体和视觉Transformer（ViT）在内的多种模型架构进行大量实验，我们发现增加模型容量并不一定能提高组织病理学数据集上的性能。我们的研究结果强调了为组织病理学图像分析专门设计定制架构的必要性，尤其是在处理有限数据集时。利用食管腺癌公共数据集，我们证明了更简单、领域特定的架构能够实现相当或更好的性能，同时最大限度地减少过拟合。
英文摘要：
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.
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