LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning

TL;DR

LIDIA is a novel deep learning framework that improves liver tumor diagnosis on multi-phase contrast-enhanced CT by iterative phase fusion and asymmetric contrastive learning, achieving high accuracy and generalizability.

Contribution

The paper introduces LIDIA, a new network combining iterative fusion and asymmetric contrastive learning for precise liver tumor diagnosis on multi-phase CT scans.

Findings

Achieved an average AUC of 93.6% on internal data.

Demonstrated strong generalizability with 89.3% AUC on external data.

Constructed a large-scale dataset of 1,921 patients and 8,138 lesions.

Abstract

The early detection and precise diagnosis of liver tumors are tasks of critical clinical value, yet they pose significant challenges due to the high heterogeneity and variability of liver tumors. In this work, a precise LIver tumor DIAgnosis network on multi-phase contrast-enhance CT, named LIDIA, is proposed for real-world scenario. To fully utilize all available phases in contrast-enhanced CT, LIDIA first employs the iterative fusion module to aggregate variable numbers of image phases, thereby capturing the features of lesions at different phases for better tumor diagnosis. To effectively mitigate the high heterogeneity problem of liver tumors, LIDIA incorporates asymmetric contrastive learning to enhance the discriminability between different classes. To evaluate our method, we constructed a large-scale dataset comprising 1,921 patients and 8,138 lesions. LIDIA has achieved an average AUC of 93.6% across eight different types of lesions, demonstrating its effectiveness. Besides, LIDIA also demonstrated strong generalizability with an average AUC of 89.3% when tested on an external cohort of 828 patients.