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

arXiv: https://arxiv.org/abs/2407.13217 · HTML: https://mlsanity.com/paper/2407.13217/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Label-Free Liver Tumor Segmentation](https://mlsanity.com/paper/2303.14869/) (2023)
- [MP-Former: Mask-Piloted Transformer for Image Segmentation](https://mlsanity.com/paper/2303.07336/) (2023)
- [Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma](https://mlsanity.com/paper/1807.02941/) (2018)
- [E2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans](https://mlsanity.com/paper/2007.09791/) (2020)
- [PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images](https://mlsanity.com/paper/2103.00274/) (2021)
- [Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network](https://mlsanity.com/paper/2307.08268/) (2023)

## Built on by (most cited first, 5 of 5)

- [Anatomy-Aware Low-Dose CT Denoising via Pretrained Vision Models and Semantic-Guided Contrastive Learning](https://mlsanity.com/paper/2508.07788/) (2025)
- [PLUS: Plug-and-Play Enhanced Liver Lesion Diagnosis Model on Non-Contrast CT Scans](https://mlsanity.com/paper/2507.03872/) (2025)
- [CECT-Mamba: a Hierarchical Contrast-enhanced-aware Model for Pancreatic Tumor Subtyping from Multi-phase CECT](https://mlsanity.com/paper/2509.12777/) (2025)
- [Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT](https://mlsanity.com/paper/2411.18290/) (2024)
- [SALIENT: Frequency-Aware Paired Diffusion for Controllable Long-Tail CT Detection](https://mlsanity.com/paper/2602.23447/) (2026)

## Abstract (arXiv)

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.