Abstract
Supervised deep learning models often achieve excellent performance within their training distribution but struggle to generalize beyond it. In cancer histopathology, for example, a convolutional neural network (CNN) may classify cancer severity accurately for cancer types represented in its training data, yet fail on related but unseen types. Although adenocarcinomas from different organs share morphological features that might support limited cross-domain generalization, addressing domain shift directly is necessary for robust performance. Domain adaptation offers a way to transfer knowledge from labeled data in one cancer type to unlabeled data in another, helping mitigate the scarcity of annotated medical images. This work evaluates cross-domain classification performance among lung, colon, breast, and kidney adenocarcinomas. A ResNet50 trained on any single adenocarcinoma achieves over 98% accuracy on its own domain but shows minimal generalization to others. Ensembling multiple supervised models does not resolve this limitation. In contrast, converting the ResNet50 into a domain adversarial neural network (DANN) substantially improves performance on unlabeled target domains. A DANN trained on labeled breast and colon data and adapted to unlabeled lung data reaches 95.56% accuracy. We also examine the impact of stain normalization on domain adaptation. Its effects vary by target domain: for lung, accuracy drops from 95.56% to 66.60%, while for breast and colon targets, stain normalization boosts accuracy from 49.22% to 81.29% and from 78.48% to 83.36%, respectively. Finally, using Integrated Gradients reveals that DANNs consistently attribute importance to biologically meaningful regions such as densely packed nuclei, indicating that the model learns clinically relevant features and can apply them to unlabeled cancer types.
Abstract (translated)
监督深度学习模型通常在训练数据分布内表现出色，但在超出此范围时难以泛化。例如，在癌症病理学中，卷积神经网络（CNN）可以准确分类其训练数据包含的癌症类型的风险等级，但对相关的未见过的类型则可能失败。尽管来自不同器官的腺癌具有共享的形态特征，这些特征可能会支持有限的跨域泛化，但是直接解决领域偏移问题对于实现稳健性能是必要的。领域适应提供了一种方法，可以从一种癌症类型的标注数据中转移知识到另一种未标记的数据上，有助于缓解医学图像注释不足的问题。这项工作评估了肺、结肠、乳腺和肾脏腺癌之间的跨域分类表现。 使用单个腺癌训练的ResNet50在自身领域内达到超过98%的准确率，但在其他领域中的泛化能力非常有限。将多个监督模型进行集成并不能解决这一限制。相比之下，将ResNet50转换为领域对抗神经网络（DANN）显著提高了未标记目标领域的性能表现。一个使用标注好的乳腺和结肠数据训练并适应于未标记肺部数据的DANN达到了95.56%的准确率。 此外，这项研究还考察了染色标准化对域适应的影响。其效果因目标领域而异：对于肺腺癌，在应用染色标准化后，准确性从95.56%下降到66.60%，而对于乳腺和结肠的目标领域，染色标准化分别将准确率从49.22%提高到81.29%，以及从78.48%提升至83.36%。 最后，通过集成梯度方法揭示了DANN模型始终倾向于生物意义显著的区域（如密集核），表明该模型学习到了临床相关的特征，并能够将其应用于未标注的癌症类型。
URL
https://arxiv.org/abs/2601.14678
