LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT

Hao Zhang; Yi Huang; Wei Liu; Yuan Gao; Zeju Li; Xu Han; Yu Shi; Ling Zhang

cs.CVOct 13, 2026

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,

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 ·

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.

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
Model	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
Model	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
$\mathcal{L}_{\textit{\text{spa}}}$	$\mathcal{L}_{\textit{\text{fre}}}$	$\mathcal{L}^{\textit{\text{cons}}}_{\textit{\text{fre}}}$	$\mathcal{L}^{\textit{\text{cons}}}_{\textit{\text{spa}}}$	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
Order	AUC	Sens	Spec	AUC	Sens	Spec	HCC	ICC	Meta	Heman	FNH	AVG
S $\rightarrow$ 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 $\rightarrow$ 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

\rightarrow

F: spatial masking followed by frequency masking. F

\rightarrow

S: frequency masking followed by spatial masking.

Figure 4: Hyper-parameter sensitivity analysis of LiFUSE on liver tumor screening and subtype diagnosis tasks.

Medical Image Analysis

Medical image analysis uses computational methods to interpret clinical images, measure anatomy and disease, and support diagnosis and treatment.

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May 15, 2026

TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT

Automated segmentation of liver lesions on non-contrast computed tomography (NCCT) is clinically important but fundamentally challenging, particularly in low-resource settings across Africa and Asia where contrast agents are frequently unavailable. Progress has been limited by the absence of annotated NCCT benchmarks. Here we describe the TriALS challenge for automated liver lesion segmentation under contrast-limited conditions, supported by a multi-centre dataset of 150 cases with four-phase CT acquisitions (600 volumes) from Egyptian and Chinese institutions. Algorithms were evaluated on 70 cases from three institutions, including an independent external cohort. The top-performing method achieved a mean venous-phase Dice of 0.754, consistent with human-level performance, yet dropped to 0.57 on NCCT. On external validation, the leading method outperformed off-the-shelf models by up to 28% in Dice on NCCT. Algorithm performance was most strongly predicted by training data scale and pre-training strategy. A cross-year comparison exposed a persistent perceptual barrier on NCCT that scaling pre-training alone cannot overcome. Data, annotations, and code are available at https://github.com/xmed-lab/TriALS.

Marawan Elbatel, Mohamed Ghonim, Jiaji Mao +62

Image Segmentation Medical Image Segmentation Computed Tomography Medical Image Benchmarks

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Oct 15, 2026

L2GSSL: Label-Efficient Local-to-Global Self-Supervised Learning for Volumetric Healthcare Data

Healthcare institutions routinely accumulate high-dimensional computed tomography (CT) volumes, but only a fraction can be annotated by experts. We present L2GSSL, a local-to-global self-supervised learning framework that leverages unlabeled volumes by decomposing pretraining into intra-slice and inter-slice representation spaces. A slice encoder learns view-invariant local features, and a global encoder aggregates the resulting tokens to model volumetric context. The stop-gradient isolates the local space from the global objective, while slice-wise execution and a three-phase schedule support training under constrained GPU memory. On LIDC-IDRI lung nodule malignancy classification, L2GSSL reaches

0.9229

AUROC with only

10\%

of the labels and

0.9377

with

25\%

, exceeding the volume-level VICReg by

0.0229

and

0.0294

, respectively. With all labels, the strongest supervised baseline exceeds L2GSSL by only

0.0044

AUROC, consistent with self-supervised pretraining providing its greatest value when annotations are limited. Without adaptation to the external LNDb cohort, a model fine-tuned on

25\%

of LIDC-IDRI obtains

0.8138

AUROC and

0.7589

AUPRC, improving over the strongest evaluated self-supervised baseline by

0.1130

and

0.0954

. Experiments across data sources, thoracic and abdominal anatomy, downstream tasks, and two backbones examine healthcare-data variety, while lesion-slice masking shows that the predictions depend on lesion-bearing content.

Po-Cheng Cheng, Hung-Hsuan Chen

Computer Science and Information Engineering, National Central University

Lung Nodule Detection Medical Image Representation Learning Self-Supervised Pre-Training Data-Efficient Learning Medical Image Classification

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Jul 13, 2026

Learning from Complementary Ultrasound Representations for Liver Disease Classification

Differentiating non-alcoholic steatohepatitis (NASH) from non-alcoholic fatty liver disease (NAFLD) using ultrasound remains challenging due to subtle tissue alterations and the limited information available in conventional B-mode imaging. In this work, we investigate whether complementary ultrasound representations derived from the same acquisition can improve NASH versus NAFLD classification. Specifically, we combine conventional B-mode ultrasound with physics-guided and local phase-based image representations and evaluate their effectiveness using self-supervised masked autoencoders (MAEs) and graph convolutional networks (GCNs). Experiments were conducted on a multi-site Mayo Clinic cohort consisting of 2,547 liver ultrasound scans from 125 patients. Compared with conventional B-mode ultrasound alone, complementary ultrasound representations consistently improved classification performance, yielding gains of up to 32.4% in accuracy and 91.2% in F1-score. Furthermore, performance improvements were consistently observed across age groups, sex, race, ethnicity,and acquisition sites.

Sabahattin Mert Daloglu, Gokce Bekar, Ceren Coskun +5

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

Medical Image Classification Ultrasound Imaging Medical Image Representation Learning Ultrasound Image Classification

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Abstract

Medical Image Analysis

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