Semi-supervised learning of functional connectome for disease classification

Overfitting is a common problem when computational models are applied on neuroimaging datasets, which are high-dimensional and small in terms of sample sizes, resulting in poor inferences such as ungeneralizable biomarkers. One way to overcome this is to pool datasets of similar diseases that are co...

Full description

Saved in:
Bibliographic Details
Main Author: Yew, Wei Chee
Other Authors: Jagath C Rajapakse
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/156535
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-156535
record_format dspace
spelling sg-ntu-dr.10356-1565352022-04-19T08:13:29Z Semi-supervised learning of functional connectome for disease classification Yew, Wei Chee Jagath C Rajapakse School of Computer Science and Engineering ASJagath@ntu.edu.sg Engineering::Computer science and engineering Overfitting is a common problem when computational models are applied on neuroimaging datasets, which are high-dimensional and small in terms of sample sizes, resulting in poor inferences such as ungeneralizable biomarkers. One way to overcome this is to pool datasets of similar diseases that are collected from other sites to augment the small dataset. However, such efforts may introduce undesirable variations due to site effects and inconsistent labeling. To mitigate these issues, two encoder-decoder-classifier architectures were proposed to carry out semi-supervised learning (SSL). Using novel multi-objective joint loss functions, end-to-end training could be applied on these architectures. The use of SSL led to a consistent increment in the model accuracy for the task of classifying between healthy subjects and patients with diseases including autism spectrum disorders (ASD) (2.5% accuracy increment in average) and attention-deficit hyperactivity disorder (ADHD) (1.0% accuracy increment in average). In addition, performing data harmonization simultaneously with SSL led to even greater improvements (+5.5% for ASD and +3.3% for ADHD in average). Biomarkers generated from the proposed method could potentially represent site-invariant biomarkers as they were shown to place more emphasis on a subset of previously discovered site-specific biomarkers. This could provide deeper insights in differentiating between site-specific and site-invariant biomarkers. The findings in this report emphasize the importance of taking both site effects and labeling inconsistencies into account when gathering datasets from multiple sites to overcome neuroimaging data paucity. In light of the increasing reliance on retrospectively aggregated open-source datasets in neuroimaging research, our architectures provide solutions to handle site effects and data paucity. Bachelor of Engineering (Computer Engineering) 2022-04-19T08:13:28Z 2022-04-19T08:13:28Z 2022 Final Year Project (FYP) Yew, W. C. (2022). Semi-supervised learning of functional connectome for disease classification. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156535 https://hdl.handle.net/10356/156535 en SCSE21-0453 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Yew, Wei Chee
Semi-supervised learning of functional connectome for disease classification
description Overfitting is a common problem when computational models are applied on neuroimaging datasets, which are high-dimensional and small in terms of sample sizes, resulting in poor inferences such as ungeneralizable biomarkers. One way to overcome this is to pool datasets of similar diseases that are collected from other sites to augment the small dataset. However, such efforts may introduce undesirable variations due to site effects and inconsistent labeling. To mitigate these issues, two encoder-decoder-classifier architectures were proposed to carry out semi-supervised learning (SSL). Using novel multi-objective joint loss functions, end-to-end training could be applied on these architectures. The use of SSL led to a consistent increment in the model accuracy for the task of classifying between healthy subjects and patients with diseases including autism spectrum disorders (ASD) (2.5% accuracy increment in average) and attention-deficit hyperactivity disorder (ADHD) (1.0% accuracy increment in average). In addition, performing data harmonization simultaneously with SSL led to even greater improvements (+5.5% for ASD and +3.3% for ADHD in average). Biomarkers generated from the proposed method could potentially represent site-invariant biomarkers as they were shown to place more emphasis on a subset of previously discovered site-specific biomarkers. This could provide deeper insights in differentiating between site-specific and site-invariant biomarkers. The findings in this report emphasize the importance of taking both site effects and labeling inconsistencies into account when gathering datasets from multiple sites to overcome neuroimaging data paucity. In light of the increasing reliance on retrospectively aggregated open-source datasets in neuroimaging research, our architectures provide solutions to handle site effects and data paucity.
author2 Jagath C Rajapakse
author_facet Jagath C Rajapakse
Yew, Wei Chee
format Final Year Project
author Yew, Wei Chee
author_sort Yew, Wei Chee
title Semi-supervised learning of functional connectome for disease classification
title_short Semi-supervised learning of functional connectome for disease classification
title_full Semi-supervised learning of functional connectome for disease classification
title_fullStr Semi-supervised learning of functional connectome for disease classification
title_full_unstemmed Semi-supervised learning of functional connectome for disease classification
title_sort semi-supervised learning of functional connectome for disease classification
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/156535
_version_ 1731235735240966144