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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Landmark-based deep multi-instance learning for brain disease diagnosis.

Mingxia Liu¹, Jun Zhang¹, Ehsan Adeli¹

¹Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599, USA.

Medical Image Analysis

|November 7, 2017

Summary

This summary is machine-generated.

This study introduces a novel landmark-based deep multi-instance learning (LDMIL) framework for improved brain disease diagnosis from MR images. The LDMIL approach outperforms existing methods by learning features directly from data, enhancing diagnostic accuracy.

Keywords:

Brain disease Convolutional neural network Landmark Multi-instance learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Conventional Magnetic Resonance (MR) image analysis for disease diagnosis involves manual region-of-interest (ROI) partitioning and pre-defined feature extraction.
Limitations in defining ROIs and extracting effective disease-related features hinder diagnostic performance in traditional methods.

Purpose of the Study:

To propose a novel landmark-based deep multi-instance learning (LDMIL) framework for enhanced brain disease diagnosis using MR images.
To overcome the limitations of manual ROI definition and pre-defined feature extraction in conventional MR image analysis.

Main Methods:

A data-driven approach to discover disease-related anatomical landmarks and associated image patches within brain MR images.
Development of an end-to-end LDMIL classifier integrating local structural information from landmark-guided patches and global structural information from all detected landmarks.

Main Results:

The LDMIL framework demonstrated superior performance compared to state-of-the-art approaches in brain disease diagnosis.
Evaluation conducted on 1526 subjects across three public datasets (ADNI-1, ADNI-2, MIRIAD).

Conclusions:

The proposed LDMIL framework offers a more effective and automated approach to brain disease diagnosis using MR imaging.
LDMIL successfully captures both local and global structural information for improved diagnostic accuracy.