bibtext: @InProceedings{ Hua_LIDIA_MICCAI2024,
                   author = { Huang, Wei and Liu, Wei and Zhang, Xiaoming and Yin, Xiaoli and Han, Xu and Li, Chunli and Gao, Yuan and Shi, Yu and Lu, Le and Zhang, Ling and Zhang, Lei and Yan, Ke },
                   title = { { LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
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-enhanced 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.
title: LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning
authors: Huang, Wei Liu, Wei Zhang, Xiaoming Yin, Xiaoli Han, Xu Li, Chunli Gao, Yuan Shi, Yu Lu, Le Zhang, Ling Zhang, Lei Yan, Ke