LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT
Authors: Hao Zhang, Yi Huang, Wei Liu, Yuan Gao, Zeju Li, Xu Han, Yu Shi, Ling Zhang, Ke Yan
Organizations: College of Biomedical Engineering, Fudan University · DAMO Academy, Alibaba Group · Hupan Lab, HangZhou · Department of Hepatobiliary and Pancreatic Surgery, First Affiliated Hospital of Zhejiang University · Department of Radiology, Shengjing Hospital of China Medical University
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.
Figures & tables
Figure 1: Comparative analysis of spatial vs. frequency domain statistics for NCCT liver lesion identification. Spatial (HU) and frequency (magnitude/phase) statistics both fail to separate normal parenchyma from lesions due to high inter-class overlap, underscoring the necessity of joint frequency-spatial modeling for robust lesion discrimination.
Figure 2: Overview of the proposed LiFUSE framework for NCCT liver analysis. To fully exploit complementary information under NCCT, LiFUSE employs: (1) Frequency-spatial hybrid-granularity masking to preserve spectral and frequency integrity while adapting to diverse lesion scales; (2) Parallel dual-path reconstruction with a shared encoder and domain-specific decoders; and (3) Frequency-spatial unified learning that enforces cycle-like cross-domain consistency.
Model
Tumor Screening
Malignant Screening
Tumor Subtype Diagnosis
AUC
Sens
Spec
AUC
Sens
Spec
HCC
ICC
Meta
Heman
FNH
AVG
nnU-Net †
96.77
89.78
93.55
91.64
89.02
85.95
92.23
99.73
95.49
97.97
88.10
94.70
UNETR †
96.53
93.01
90.78
83.76
73.17
89.26
89.49
99.41
90.96
97.91
87.68
93.09
U-Mamba †
95.83
91.40
93.55
92.16
86.59
89.26
82.48
94.65
91.26
97.01
81.98
89.48
MAE3D ‡
97.82
93.01
90.32
93.49
92.56
89.16
91.89
99.59
97.89
96.81
85.87
94.41
MiM ‡
97.53
91.40
92.63
94.21
92.68
90.91
92.56
99.62
97.10
97.11
83.53
93.98
Table 1: Fine-tuning performance comparison for liver tumor screening and subtype diagnosis on the internal test set.
Model
Steatosis Diagnosis
Cirrhosis Diagnosis
UNIFESP Diagnosis
AUC
Sens
Spec
AUC
Sens
Spec
AUC
Sens
Spec
nnU-Net †
94.36
86.96
84.27
90.56
82.35
87.13
83.80
76.32
68.42
UNETR †
93.77
89.13
80.90
91.09
85.29
91.09
81.02
68.42
78.95
U-Mamba †
95.60
86.96
93.26
94.21
85.29
93.07
86.57
78.95
78.95
MAE3D ‡
93.92
89.13
88.76
96.07
91.18
92.08
87.53
76.32
84.21
MiM ‡
94.65
84.78
89.89
94.12
82.35
93.07
90.16
84.21
89.47
Table 2: Fine-tuning performance comparison for diffuse liver diseases diagnosis on internal and public test sets.
Loss
Tumor Screening
Malignant Screening
Tumor Subtype Diagnosis
Lspa
Lfre
Lfrecons
Lspacons
AUC
Sens
Spec
AUC
Sens
Spec
Malig
Benign
AVG
✓
96.75
94.09
90.32
92.34
93.90
89.26
97.42
91.44
95.03
✓
97.60
91.40
90.78
93.29
92.68
90.08
96.37
90.45
94.00
✓
✓
97.20
91.94
92.63
93.17
90.91
87.36
96.65
91.73
94.69
✓
✓
97.97
92.47
89.40
93.63
87.80
93.93
97.36
90.55
94.64
✓
✓
97.10
92.47
92.63
94.00
95.12
88.43
97.11
94.31
95.99
Table 3: Ablation study on individual loss functions for liver tumor screening and subtype diagnosis on internal test set.
Figure 3: Comparative visualization of the t-SNE embedding of five tumor subtypes and normal liver tissue in the feature space. While single-domain embeddings show severe inter-class overlap among all six classes, LiFUSE’s unified learning yields distinctly separable clusters with reduced intra-class variance.
Order
Tumor Screening
Malignant Screening
Tumor Subtype Diagnosis
AUC
Sens
Spec
AUC
Sens
Spec
HCC
ICC
Meta
Heman
FNH
AVG
S → F
98.18
90.86
93.09
92.96
90.24
89.26
93.38
99.71
97.52
98.90
87.89
95.48
F → S
98.35
94.62
93.09
95.09
96.34
90.08
96.52
99.66
97.84
98.57
88.80
96.28
Table 4: Analysis of masking order on tumor screening and subtype diagnosis tasks. S → F: spatial masking followed by frequency masking. F → S: frequency masking followed by spatial masking.
Figure 4: Hyper-parameter sensitivity analysis of LiFUSE on liver tumor screening and subtype diagnosis tasks.
PONS Incorporated, Newark, NJ, USA · Department of Radiology, Mayo Clinic, Jacksonville, FL, USA · Department of Radiology, Department of Medicine, University of British Columbia, Vancouver, Canada