  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.
