Unsupervised medical image classification by combining case-based classifiers Thien Anh Dinh 1, Tomi...
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![Page 1: Unsupervised medical image classification by combining case-based classifiers Thien Anh Dinh 1, Tomi Silander 1, Bolan Su 1, Tianxia Gong Boon Chuan Pang.](https://reader035.fdocuments.in/reader035/viewer/2022062717/56649e375503460f94b26810/html5/thumbnails/1.jpg)
Unsupervised medical image classification by combining
case-based classifiers
Thien Anh Dinh1, Tomi Silander1, Bolan Su1, Tianxia Gong
Boon Chuan Pang2, Tchoyoson Lim2, Cheng Kiang Lee2
Chew Lim Tan1,Tze-Yun Leong1
1National University of Singapore2National Neuroscience Institute
3Bioinformatics Institute, Singapore
![Page 2: Unsupervised medical image classification by combining case-based classifiers Thien Anh Dinh 1, Tomi Silander 1, Bolan Su 1, Tianxia Gong Boon Chuan Pang.](https://reader035.fdocuments.in/reader035/viewer/2022062717/56649e375503460f94b26810/html5/thumbnails/2.jpg)
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Automated medical image annotation
• Huge amount of valuable data available in medical image databases
• Not fully utilized for medical treatment, research and education
• Medical image annotation:
1. To extract knowledge from images to facilitate text-based retrieval of relevant images
2. To provide a second source of opinions for clinicians on abnormality detection and pathology classification
![Page 3: Unsupervised medical image classification by combining case-based classifiers Thien Anh Dinh 1, Tomi Silander 1, Bolan Su 1, Tianxia Gong Boon Chuan Pang.](https://reader035.fdocuments.in/reader035/viewer/2022062717/56649e375503460f94b26810/html5/thumbnails/3.jpg)
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Problem
• Flowchart of current methods
• Challenges in current methods• Highly sensitive and accurate segmentation• Extracting domain knowledge• Automatic feature selection
• Time-consuming manual adjustment process
reduces usages of medical image annotation systems
Extracting features
Selecting discriminative features
Building classifiers Labeling
![Page 4: Unsupervised medical image classification by combining case-based classifiers Thien Anh Dinh 1, Tomi Silander 1, Bolan Su 1, Tianxia Gong Boon Chuan Pang.](https://reader035.fdocuments.in/reader035/viewer/2022062717/56649e375503460f94b26810/html5/thumbnails/4.jpg)
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Objective
• An automated pathology classification system for volumetric brain image slices
• Main highlights
1. Eliminates the need for segmentation and semantic or annotation-based feature selection
• Reduces the amount of manual work for constructing an annotation system
2. Extracts automatically and efficiently knowledge from images
3. Improves the utilization of medical image databases
![Page 5: Unsupervised medical image classification by combining case-based classifiers Thien Anh Dinh 1, Tomi Silander 1, Bolan Su 1, Tianxia Gong Boon Chuan Pang.](https://reader035.fdocuments.in/reader035/viewer/2022062717/56649e375503460f94b26810/html5/thumbnails/5.jpg)
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System overview
• Case-based classifier• Gabor filters
• Non domain specific features
• Localized low-level features
• Ensemble learning• Set of classifiers• Each classifier with a
random subset of features• Final classification: an
aggregated result
![Page 6: Unsupervised medical image classification by combining case-based classifiers Thien Anh Dinh 1, Tomi Silander 1, Bolan Su 1, Tianxia Gong Boon Chuan Pang.](https://reader035.fdocuments.in/reader035/viewer/2022062717/56649e375503460f94b26810/html5/thumbnails/6.jpg)
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Sparse representation-based classifier
• Sparse representation-based classifier (SRC) proposed by Wright et al. for face recognition task
• Non-parametric sparse representation classifier
• SRC consists of two stages
1. Reconstructing: a test image as a linear combination of a small number of training images
2. Classifying: evaluating how the images belonging to different classes contribute to the reconstruction of the test image
![Page 7: Unsupervised medical image classification by combining case-based classifiers Thien Anh Dinh 1, Tomi Silander 1, Bolan Su 1, Tianxia Gong Boon Chuan Pang.](https://reader035.fdocuments.in/reader035/viewer/2022062717/56649e375503460f94b26810/html5/thumbnails/7.jpg)
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Sparse representation-based classifier(Wright et al.)
![Page 8: Unsupervised medical image classification by combining case-based classifiers Thien Anh Dinh 1, Tomi Silander 1, Bolan Su 1, Tianxia Gong Boon Chuan Pang.](https://reader035.fdocuments.in/reader035/viewer/2022062717/56649e375503460f94b26810/html5/thumbnails/8.jpg)
y ≈ a7x7 + a23x23 + a172x172 + a134x134 + a903x903
Image databases x1, x2,…, x1000
Sparsereconstruction
Classresiduals
New data item
y ≈ a7x7 + a23x23 + a172x172 + a134x134 + a903x903
r1 = || y – (a7x7 + a172x172 + a132x134)||2
r2 = || y – (a23x23 + a903x903)||2
![Page 9: Unsupervised medical image classification by combining case-based classifiers Thien Anh Dinh 1, Tomi Silander 1, Bolan Su 1, Tianxia Gong Boon Chuan Pang.](https://reader035.fdocuments.in/reader035/viewer/2022062717/56649e375503460f94b26810/html5/thumbnails/9.jpg)
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Ensemble of weak classifiers
• Combine multiple weak classifiers
• Take class specific residuals as confidence measures
The smaller the residual for the class, the better we construct the test by just using the samples from that class
• To classify image y, compute average class-specific residuals of all W weak classifiers
![Page 10: Unsupervised medical image classification by combining case-based classifiers Thien Anh Dinh 1, Tomi Silander 1, Bolan Su 1, Tianxia Gong Boon Chuan Pang.](https://reader035.fdocuments.in/reader035/viewer/2022062717/56649e375503460f94b26810/html5/thumbnails/10.jpg)
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Domain
• Automatically annotate CT brain images for traumatic brain injury (TBI)
TBI: major cause of death and disability
• Several types of hemorrhages:
i. Extradural hematoma (EDH)
ii. Subdural hematoma (SDH)
iii. Intracerebral hemorrage (ICH)
iv. Subarachnoid hemorrhage (SAH)
v. Intraventricular hematoma (IVH) Subdural hematomaExtradural hematoma
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Data
• CT brain scans of 103 patients• Each scan:
• Volumetric stack of 18-30 images (slices)
• Image resolution: 512 x 512 pixels
• Manually assigned a hematoma type extracted from its medical text report
SDH57%ED
H23%
ICH21%
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12A volumetric CT brain scan with 19 slices
![Page 13: Unsupervised medical image classification by combining case-based classifiers Thien Anh Dinh 1, Tomi Silander 1, Bolan Su 1, Tianxia Gong Boon Chuan Pang.](https://reader035.fdocuments.in/reader035/viewer/2022062717/56649e375503460f94b26810/html5/thumbnails/13.jpg)
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Experimental setup
• Compared performances of• SRC vs. SVM vs. SVM + feature selection• With/without ensemble learning
• Run stratified ten-fold cross-validation 50 times with different random foldings
• Measured the average precisions and recalls
• Separated training and testing dataset at the case level
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Experimental results
![Page 15: Unsupervised medical image classification by combining case-based classifiers Thien Anh Dinh 1, Tomi Silander 1, Bolan Su 1, Tianxia Gong Boon Chuan Pang.](https://reader035.fdocuments.in/reader035/viewer/2022062717/56649e375503460f94b26810/html5/thumbnails/15.jpg)
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Experimental results when varying the ensemble size
Average precision and recall of classifiers when varying the ensemble size (number of features = 1000)
![Page 16: Unsupervised medical image classification by combining case-based classifiers Thien Anh Dinh 1, Tomi Silander 1, Bolan Su 1, Tianxia Gong Boon Chuan Pang.](https://reader035.fdocuments.in/reader035/viewer/2022062717/56649e375503460f94b26810/html5/thumbnails/16.jpg)
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Experimental results when varying the number of features per classifier
Average precisions and recalls of classifiers when varying number of features (ensemble size = 50)
![Page 17: Unsupervised medical image classification by combining case-based classifiers Thien Anh Dinh 1, Tomi Silander 1, Bolan Su 1, Tianxia Gong Boon Chuan Pang.](https://reader035.fdocuments.in/reader035/viewer/2022062717/56649e375503460f94b26810/html5/thumbnails/17.jpg)
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Conclusion
• Ensemble classification framework with sparse Gabor-feature based classifier• Eliminates the requirement for segmentation and
supervised feature selection• Reduces the need for manual adjustment• Achieves reasonable results compared to
segmentation dependent techniques (Gong et al.)
• Limitation• Longer classification time when dealing with large
training data• Manual weighting needed for imbalanced data
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THANK YOU
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Gabor features
• Localize low level features from an input image
• Resemble the primitive features extracted by human visual cortex
• Extract edge like features in different scales and orientations at different locations of the image
• Create a Gabor filter bank with 5 frequencies and 8 orientations
A 128 x 128 grayscale image: 655360 features
Randomly select 4000 Gabor features to form a feature subspace