Continual learning strategies for cancer-independent detection of lymph node metastases

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.

Automated summary

The researchers investigated how to efficiently utilize existing high-quality datasets in multi-task settings and strategies such as prevention of catastrophic forgetting for breast, colon, and head-and-neck cancer metastasis detection in lymph nodes. The results showed state-of-the-art performance in colon and head-and-neck cancer metastasis detection tasks. They also demonstrated the effectiveness of adapting networks from one cancer type to another to obtain multi-task metastasis detection networks, and that leveraging existing high-quality datasets can significantly improve performance on new target tasks while mitigating catastrophic forgetting.