arXiv：cs.AI· Hao Zhang, Yi Huang, Wei Liu, Yuan Gao, Zeju Li, Xu Han, Yu Shi, Ling Zhang, Ke Yan·· 20 小时前
LiFUSE：面向非增强 CT 肝病分析的频率-空间统一自监督预训练
LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT
AI 导读
针对非增强 CT（NCCT）组织对比度低、肝脏病灶难以识别的问题，研究者提出自监督预训练方法 LiFUSE，其频率分支用随机带阻掩码与频谱重建捕捉病灶与周围组织的细微差异，空间分支用混合粒度掩码体素重建适配不同大小与形态的病灶，并通过跨域一致性方案耦合两分支。在内部队列的三项临床相关任务及一个公开目标域迁移设置上，LiFUSE 一致优于强基线，该工作已被 BIBM 2026 接收。
正文
Abstract:Given the growing global burden of liver tumors and diffuse liver diseases, non-contrast computed tomography (NCCT) is increasingly used for opportunistic screening because of its low cost and favorable safety profile. However, downstream tasks such as tumor detection and fine-grained disease diagnosis remain challenging due to NCCT's inherently low tissue contrast, lack of hemodynamic enhancement, and complex anatomical background. These limitations make subtle, heterogeneous, and multifocal hepatic lesions difficult to identify. Although large-scale self-supervised pretraining can learn robust representations from unlabeled NCCT scans, conventional voxel-wise reconstruction does not fully exploit the complementary information available in NCCT. Under weak NCCT contrast, low-level intensity patterns become less informative for pathology recognition, limiting the capture of lesion-specific discriminative features. In this paper, we propose the Liver Frequency-Spatial Unified Self-Supervised Pretraining (LiFUSE). The frequency branch applies random band-stop masking and spectrum reconstruction to encourage the model to capture subtle differences between lesions and surrounding tissues, while the spatial branch performs hybrid-granularity masked volumetric reconstruction to accommodate lesions of diverse sizes and morphologies. Finally, a cross-domain consistency scheme couples the spatial and frequency branches, yielding more discriminative representations for hepatic structures and lesions. Extensive experiments on three clinically relevant tasks across internal cohorts and a public target-domain transfer setting show that LiFUSE consistently outperforms strong baselines. These results suggest that unified frequency-spatial learning can improve liver disease analysis on NCCT, highlighting its potential for large-scale screening.
| Comments: | Accepted at the 2026 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2026) |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.18243 [cs.CV] |
| (or arXiv:2610.18243v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18243 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hao Zhang [view email]
[v1]
Tue, 13 Oct 2026 12:08:56 UTC (6,320 KB)
来源：arXiv：cs.AI · arxiv.org