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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Cross-scale multi-instance learning for pathological image diagnosis.

Ruining Deng¹, Can Cui¹, Lucas W Remedios¹

¹Vanderbilt University, Nashville, TN 37215, USA.

Medical Image Analysis

|March 1, 2024

Summary

This summary is machine-generated.

This study introduces a novel cross-scale multiple-instance learning (MIL) algorithm for digital pathology. It effectively integrates multi-scale information from whole slide images (WSIs) to improve diagnostic accuracy.

Keywords:

Attention mechanism Multi-instance learning Multi-scale Pathology

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Area of Science:

Digital Pathology
Computational Pathology
Medical Image Analysis

Background:

Analyzing high-resolution whole slide images (WSIs) in digital pathology presents challenges due to information across multiple scales.
Multi-instance learning (MIL) is used for WSIs by classifying image patches, but often ignores crucial inter-scale information vital for diagnosis.

Purpose of the Study:

To propose a novel cross-scale MIL algorithm for pathological image diagnosis.
To explicitly aggregate inter-scale relationships within a single MIL network.
To enhance diagnostic accuracy by integrating multi-scale information.

Main Methods:

Developed a novel cross-scale MIL (CS-MIL) algorithm to integrate multi-scale information and inter-scale relationships.
Created and released a toy dataset with scale-specific morphological features for visualizing cross-scale attention.
Applied the CS-MIL algorithm to in-house and public pathological datasets.

Main Results:

The proposed CS-MIL algorithm effectively integrates multi-scale information and inter-scale relationships.
Differential cross-scale attention was examined and visualized using the created toy dataset.
The CS-MIL strategy demonstrated superior performance on both in-house and public datasets compared to existing methods.

Conclusions:

The novel cross-scale MIL approach effectively leverages inter-scale relationships in WSIs for improved pathological diagnosis.
The CS-MIL algorithm offers a simple yet powerful strategy for enhancing diagnostic accuracy in digital pathology.
The study provides a valuable contribution to the field with a publicly available implementation and dataset.