Domain adaptation strategies for cancer-independent detection of lymph node metastases

TL;DR

This paper explores domain adaptation techniques to improve lymph node metastasis detection across different cancer types, leveraging existing datasets to enhance multi-task learning and mitigate catastrophic forgetting.

Contribution

It introduces effective domain adaptation strategies, including regularization, for multi-cancer metastasis detection, achieving state-of-the-art results.

Findings

State-of-the-art performance on colon and head-and-neck metastasis detection

Repeated adaptation improves multi-task detection networks

Leveraging existing datasets boosts target task performance

Abstract

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 colon and head-and-neck cancer metastasis detection in lymph nodes. Our results show state-of-the-art performance on both cancer metastasis detection tasks. Furthermore, we show the effectiveness of repeated adaptation of networks from one cancer type to another to obtain multi-task metastasis detection networks. Last, we show that leveraging existing high-quality datasets can significantly boost performance on new target tasks and that catastrophic forgetting can be effectively mitigated using regularization.