最近，大规模高质量的公共数据集已经导致卷积神经网络的发展，它们可以在病理专家的水平上检测乳腺癌淋巴结转移。许多癌症，不管是哪个部位，都可以转移到淋巴结。然而，为每种癌症类型收集和注释高容量、高质量的数据集是具有挑战性的。在本文中，我们研究如何在密切相关的任务的多任务设置中最有效地利用现有的高质量数据集。具体而言，我们将探讨不同的训练和领域自适应策略，包括防止灾难性遗忘，用于乳腺、结肠和头颈癌转移在淋巴结的检测。我们的结果显示，在结肠和头颈癌转移检测任务中表现良好。我们展示了从一个癌症类型适应到另一个癌症类型以获取多任务转移检测网络的有效性。此外，我们还展示了利用现有高质量数据集可以显著提高新目标任务的性能，而灾难性遗忘可以有效地缓解。最后，我们比较了不同的缓解策略。opyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.

Recently, large, high-quality public datasets have led to the development of convolutional neural networks that can detect lymph node metastases of breast cancer at the level of expert pathologists. Many cancers, regardless of the site of origin, can metastasize to lymph nodes. However, collecting and annotating high-volume, high-quality datasets for every cancer type is challenging. In this paper we investigate how to leverage existing high-quality datasets most efficiently in multi-task settings for closely related tasks. Specifically, we will explore different training and domain adaptation strategies, including prevention of catastrophic forgetting, for breast, colon and head-and-neck cancer metastasis detection in lymph nodes. Our results show state-of-the-art performance on colon and head-and-neck cancer metastasis detection tasks. We show the effectiveness of adaptation of networks from one cancer type to another to obtain multi-task metastasis detection networks. Furthermore, we show that leveraging existing high-quality datasets can significantly boost performance on new target tasks and that catastrophic forgetting can be effectively mitigated.Last, we compare different mitigation strategies.Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.