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Updated: Sep 11, 2025

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

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TS-DENet: a transferable self-supervised learning method for multi-modal fluorescence image denoising.

Liangliang Huang, Zhong Wen, Zhaokai Wang

|August 12, 2025

Summary

This summary is machine-generated.

This study introduces TS-DENet, a novel deep learning framework that enhances fluorescence images by reducing noise and sharpening edges. It improves diagnostic accuracy for early-stage diseases in real-world conditions.

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Area of Science:

Biomedical Imaging
Medical Diagnostics
Deep Learning Applications

Background:

Fluorescence imaging aids early disease detection and therapy monitoring but suffers from noise and quality degradation due to variable conditions.
Deep learning (DL) shows promise for image enhancement, yet faces limitations with limited data and multi-modal fluorescence imaging challenges.
Current DL methods struggle with generalizability and transferability across diverse biomedical imaging tasks.

Purpose of the Study:

To develop an advanced deep learning framework for improving fluorescence image quality in biomedical applications.
To address challenges of noise, low quality, and limited data in multi-modal fluorescence imaging.
To enhance the diagnostic utility of fluorescence imaging for early-stage neoplasia detection and therapy monitoring.

Main Methods:

Proposed a two-stage deep denoising and edge enhancement framework (TS-DENet).
Utilized large-dataset-based pre-training with a masked reconstruction task to learn features.
Implemented domain-specific fine-tuning for focused denoising and edge enhancement.
Validated performance across diverse data regimes and an in vivo endoscopic system.

Main Results:

TS-DENet achieved state-of-the-art performance in various data scenarios, outperforming existing DL methods.
Demonstrated superior generalizability and transferability compared to other DL-based approaches.
Successfully applied to enhance fluorescence images of rat gastric tissues using a multimode fiber endoscopic system.

Conclusions:

TS-DENet offers a robust solution for improving fluorescence image quality, overcoming limitations of noise and blur.
The framework shows significant potential for enhancing clinical diagnostic capabilities in real-world settings.
This approach advances the application of deep learning in biomedical fluorescence imaging for improved patient outcomes.