Unsupervised Domain Adaptation Using Adversarial Learning and Maximum Square Loss for Liver Tumors Detection in Multi-phase CT Images

Automatic and efficient liver tumor detection in multi-phase CT images is essential in computer-aided diagnosis of liver tumors. Nowadays, deep learning has been widely used in medical applications. Normally, deep learning-based AI systems need a large quantity of training data, but in the medical field, acquiring sufficient training data with high-quality annotations is a significant challenge. To solve the lack of training data issue, domain adaptation-based methods have recently been developed as a technique to bridge the domain gap across datasets with different feature characteristics and data distributions. This paper presents a domain adaptation-based method for detecting liver tumors in multi-phase CT images. We adopt knowledge for model learning from PV phase images to ART and NC phase images. Clinical Relevance— To minimize the domain gap we employ an adversarial learning scheme with the maximum square loss for mid-level output feature maps using an anchorless detector. Experiments show that our proposed method performs much better for various CT-phase images than normal training