Learning Transferability in Deep Segmentation of Liver Metastases

Abstract

The ability to transfer knowledge and models across dif-ferent datasets and clinical scenarios is of paramount impor-tance in medical imaging. This is especially true for liverlesion segmentation which is crucial for pre-operative plan-ning and treatment follow-up. Despite the progress of deeplearning algorithms using Transformers, automatically seg-menting small hepatic metastases remains a persistent chal-lenge. This can be attributed to the degradation of small struc-tures due to the intrinsic process of feature down-samplinginherent to many architectures as well as class imbalance.While similar challenges have been observed for liver tumorsoriginated from hepatocellular carcinoma, their manifestationin the context of liver metastasis delineation remains under-investigated. Through comprehensive experiments, this paperaims to bridge this gap and to demonstrate the impact of var-ious transfer learning schemes from off-the-shelf datasets toa dataset containing liver metastases only. Our scale-specificevaluation reveals that models trained from scratch or withdomain-specific pre-training demonstrate greater proficiency.

Tasks

Reproductions