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Related Experiment Video

Updated: Apr 29, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Multiple instance learning for classification of dementia in brain MRI.

Tong Tong¹, Robin Wolz¹, Qinquan Gao¹

¹Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen's Gate, London SW7 2AZ, UK.

Medical Image Analysis

|May 27, 2014

Summary

This summary is machine-generated.

This study introduces a novel multiple instance learning (MIL) approach for detecting Alzheimer's disease (AD) and mild cognitive impairment (MCI) using brain MRI scans. The method accurately distinguishes between healthy individuals and AD patients, and between stable and progressive MCI.

Keywords:

Alzheimer’s disease Classification Multiple instance learning Structural MR imaging

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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

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Published on: June 9, 2018

Area of Science:

Neuroimaging
Machine Learning
Medical Diagnostics

Background:

Machine learning is crucial for identifying brain abnormalities in MRI scans for neurological disease diagnosis.
Detecting Alzheimer's disease (AD) and mild cognitive impairment (MCI) presents challenges due to ambiguous imaging features.

Purpose of the Study:

To apply a multiple instance learning (MIL) method for improved detection of AD and MCI.
To address label ambiguity in brain MRI data for neurodegenerative disease classification.

Main Methods:

Extracted local intensity patches as features from structural brain MRI data.
Employed a graph-based multiple instance learning (MIL) framework to model relationships among image patches.
Utilized baseline MR images from 834 subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.

Main Results:

Achieved 89% accuracy in classifying AD patients versus healthy controls.
Attained 70% accuracy in differentiating stable MCI from progressive MCI.
Demonstrated comparable or superior performance against two state-of-the-art methods on the same dataset.

Conclusions:

The proposed graph-based MIL method offers a robust framework for detecting and predicting neurodegenerative diseases like AD and MCI.
This approach effectively handles label ambiguity in medical image analysis.
Provides a valuable alternative for early diagnosis and prognosis in neurological disorders.