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Updated: Jan 7, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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LivSCP: Improving Liver Fibrosis Classification Through Supervised Contrastive Pretraining.

Yogita Dubey¹, Aditya Bhongade¹, Punit Fulzele²

¹Department of Electronics & Telecommunication Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India.

Diagnostics (Basel, Switzerland)

|December 30, 2025

Summary

This summary is machine-generated.

A new training method, LivSCP, enhances non-invasive liver fibrosis classification using ultrasound scans. It achieves state-of-the-art results without altering model architecture, ideal for limited data scenarios.

Keywords:

contrastive learning liver fibrosis pretraining supervised contrastive learning vision transformer

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Area of Science:

Medical Imaging
Artificial Intelligence
Hepatology

Background:

Deep learning models are used for non-invasive liver fibrosis classification via ultrasound.
Performance improvements have plateaued despite advancements in model architectures and training methods.
A need exists for sophisticated methods to enhance classification accuracy.

Purpose of the Study:

To introduce LivSCP, a novel training method for liver fibrosis classification.
To improve classification accuracy beyond traditional supervised learning (SL).
To provide a solution for settings with limited labeled data and computational resources.

Main Methods:

Proposed LivSCP training method for liver fibrosis classification.
No modifications to existing network architectures or optimizers are required.
Evaluated against a baseline Vision Transformer with SL and other models.

Main Results:

Achieved state-of-the-art performance with 98.10% accuracy, precision, recall, and F1-score.
Attained an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.9972.
Demonstrated effectiveness without network architecture changes, suitable for low-data environments.

Conclusions:

Successfully developed a training method (LivSCP) for liver fibrosis classification in low-data and computation settings.
LivSCP outperforms baseline and multiple models, establishing state-of-the-art performance.
The method is advantageous for resource-constrained scenarios in medical image analysis.