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Related Experiment Video

Updated: May 30, 2025

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

Full fine-tuning strategy for endoscopic foundation models with expanded learnable offset parameters.

Minghan Dong^1,2, Xiangwei Zheng^1,2, Xia Zhang³

¹School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, People's Republic of China.

Biomedical Physics & Engineering Express

|January 27, 2025

Summary

This summary is machine-generated.

A new method, Extended Learnable Offset Parameter (ELOP), enhances endoscopic video analysis for detecting complex lesions like gastrointestinal metaplasia (GIM). ELOP significantly improves diagnostic accuracy in endoscopic imaging.

Keywords:

endoscopic foundation model fine-tuning loss function offset parameters

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Precision Measurements and Parametric Models of Vertebral Endplates

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Published on: September 17, 2019

Area of Science:

Medical imaging
Artificial intelligence in medicine
Endoscopic diagnostics

Background:

Endoscopic video analysis is vital for diagnosing diseases and guiding minimally invasive surgery.
Current Endoscopic Foundation Models (Endo-FM) struggle with detecting complex lesions like gastrointestinal metaplasia (GIM) due to indistinct features.

Purpose of the Study:

To improve the performance of Endoscopic Foundation Models (Endo-FM) for detecting challenging lesions in endoscopic videos.
Introduce a novel fine-tuning strategy, the Extended Learnable Offset Parameter (ELOP), to enhance lesion detection accuracy.

Main Methods:

Implemented a fully fine-tuning strategy incorporating an Extended Learnable Offset Parameter (ELOP) into the input space.
Developed a new loss function combining cross-entropy and focal loss to focus on difficult-to-classify samples.
Validated the ELOP strategy on a private gastrointestinal metaplasia (GIM) dataset and a public polyp detection dataset.

Main Results:

The ELOP strategy significantly improved detection accuracy compared to the original Endo-FM.
Achieved accuracy enhancements of 6.25% on the GIM dataset and 3.75% on the polyp detection dataset.
Demonstrated ELOP's effectiveness in addressing the challenges of detecting lesions with unclear boundaries.

Conclusions:

The Extended Learnable Offset Parameter (ELOP) offers a robust solution for precise lesion detection in endoscopic videos.
ELOP enhances diagnostic capabilities, particularly for complex conditions like gastrointestinal metaplasia (GIM).
This approach contributes to more accurate diagnoses through improved endoscopic video analysis.