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Enhancing Instance Feature Representation: A Foundation Model-Based Multi-Instance Approach for Neonatal Retinal

Jie Guo, Keyi Wang, Guangshuang Tan

IEEE Transactions on Bio-Medical Engineering

|September 22, 2025

Summary

This summary is machine-generated.

This study introduces Learnable Dense to Global Multiple Instance Learning (LD2G-MIL) for analyzing multiple neonatal fundus images. The method improves detection of neonatal ocular pathologies, outperforming existing techniques.

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Area of Science:

Medical Imaging
Ophthalmology
Computer Vision

Background:

Automated analysis of neonatal fundus images is challenging due to subtle retinal features and the need for multiple image views.
Current methods often analyze single images, limiting accuracy in detecting minute neonatal retinal lesions.

Purpose of the Study:

To develop an automated method for comprehensive screening of neonatal ocular pathologies using multiple fundus images.
To introduce an enhanced Multiple Instance Learning (MIL) approach for improved neonatal retinal image analysis.

Main Methods:

Proposed Learnable Dense to Global Multiple Instance Learning (LD2G-MIL) method.
Focus on generating improved instance-level representations co-optimized with MIL targets.
Incorporated a bag prior-based similarity loss (BP loss) mechanism.

Main Results:

The LD2G-MIL method demonstrated superior performance in neonatal retinal screening.
Outperformed state-of-the-art generic and specialized methods on the NFI dataset.
Validated on the extensive Neonatal Fundus Images (NFI) dataset (115,621 images).

Conclusions:

LD2G-MIL offers a robust and effective solution for analyzing multiple neonatal fundus images.
The approach enhances the detection of neonatal ocular pathologies, addressing limitations of single-image analysis.
Publicly available code and models facilitate further research and clinical application.