论文标题
从现代CNN到视觉变压器：组织病理学中深度学习模型的性能，鲁棒性和分类策略
From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology
论文作者
论文摘要
尽管机器学习目前正在改变组织病理学领域，但该领域缺乏对基于基本但互补质量需求的最先进模型的全面评估，而不是仅仅是分类的准确性。为了填补这一空白，我们开发了一种新的方法，可以广泛评估广泛的分类模型，包括最近的视觉变形金刚和卷积神经网络，例如：Convnext，Resnet（BIT），Inception，Inception，Vit和Swin Transformer，具有有监督或自私自利的预识训练。我们对五个广泛使用的组织病理学数据集进行了彻底测试，这些模型包含乳腺，胃和结直肠癌的整个幻灯片图像，并使用图像到图像翻译模型开发了一种新颖的方法，以评估癌症分类模型针对染色变异的稳健性。此外，我们将现有的可解释性方法扩展到了以前未研究的模型，并系统地揭示了可以将模型分类策略的见解，这些策略可以转移到未来的模型体系结构中。
While machine learning is currently transforming the field of histopathology, the domain lacks a comprehensive evaluation of state-of-the-art models based on essential but complementary quality requirements beyond a mere classification accuracy. In order to fill this gap, we developed a new methodology to extensively evaluate a wide range of classification models, including recent vision transformers, and convolutional neural networks such as: ConvNeXt, ResNet (BiT), Inception, ViT and Swin transformer, with and without supervised or self-supervised pretraining. We thoroughly tested the models on five widely used histopathology datasets containing whole slide images of breast, gastric, and colorectal cancer and developed a novel approach using an image-to-image translation model to assess the robustness of a cancer classification model against stain variations. Further, we extended existing interpretability methods to previously unstudied models and systematically reveal insights of the models' classifications strategies that can be transferred to future model architectures.
