Adaptive Latent Transformation (ALT) for classification of resting state - FMRI

Aradhya, AMS and Sharma, G and Pratama, M and Sundaram, S (2022) Adaptive Latent Transformation (ALT) for classification of resting state - FMRI. In: 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, 4-7 December 2022, Singapore, pp. 206-210.

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Abstract

Classification of resting state - functional Magnetic Resonance Imaging (rs-fMRI) data using deep learning algorithms is a challenging problem. Previous studies using latent space transformations to project the data into a discriminant space have shown promising results. However, their reliance on mathematical modelling of the latent space have limited their performance and generalization ability. Learnable latent space transformations have provided a new approach to solve such problems. In this paper, Adaptive Latent Transformation (ALT) method is proposed to address the shortcomings in literature. The ALT proposes an adaptable latent space transformation that can project the input data into a discriminant latent space of any pre-determined dimension using a learnable projection function. The ALT latent space transformation function is integrated into a convolutional neural network algorithm and its weights are updated using the backpropagation based learning strategy to minimize the error in the classification performance. Further, to prevent overfitting of the deep network classifier a novel Confidence based Sample Selection Strategy (CSSS) is proposed to selectively train the network with information-rich samples. The performance of the ALT classifier is evaluated using the ADHD200 rs-fMRI benchmark dataset over a 10 fold cross-validation study. © 2022 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Benchmarking; Convolutional neural networks; Deep learning; Learning algorithms; Magnetic resonance imaging; Metadata, ADHD200; Attention deficit hyperactivity disorder; Discriminant spaces; Functional magnetic resonance imaging; Latent space transformation; Performance; Resonance imaging data; Resting state; Resting-state functional magnetic resonance imaging; Space transformations, Classification (of information)
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	25 Feb 2023 08:36
Last Modified:	25 Feb 2023 08:36
URI:	https://eprints.iisc.ac.in/id/eprint/80711

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