计算机科学
杠杆(统计)
卷积神经网络
任务(项目管理)
乳腺癌
人工智能
深度学习
机器学习
域适应
适应(眼睛)
癌症
医学
内科学
神经科学
管理
分类器(UML)
经济
生物
作者
Péter Bándi,Maschenka Balkenhol,Marcory van Dijk,Michel Kok,Bram van Ginneken,Jeroen van der Laak,Geert Litjens
标识
DOI:10.1016/j.media.2023.102755
摘要
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.
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