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Multi-Modal Graph Neural Network with Transformer-Guided Adaptive Diffusion for Preclinical Alzheimer Classification

Jaeyoon Sim, Minjae Lee, Guorong Wu, Won Hwa Kim

C International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Marrakesh, Morocco, 2024

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The graphical representation of the brain offers critical insights into diagnosing and prognosing neurodegenerative disease via relationships between regions of interest (ROIs). Despite recent emergence of various Graph Neural Networks (GNNs) to effectively capture the relational information, there remain inherent limitations in interpreting the brain networks. Specifically, convolutional approaches ineffectively aggregate information from distant neighborhoods, while attention-based methods exhibit deficiencies in capturing node-centric information, particularly in retaining critical characteristics from pivotal nodes. These shortcomings reveal challenges for identifying disease-specific variation from diverse features from different modalities. In this regards, we propose an integrated framework guiding diffusion process at each node by a downstream transformer where both short- and long-range properties of graphs are aggregated via diffusion-kernel and multi-head attention respectively. We demonstrate the superiority of our model by improving performance of pre-clinical Alzheimer’s disease (AD) classification with various modalities. Also, our model adeptly identifies key ROIs that are closely associated with the preclinical stages of AD, marking a significant potential for early diagnosis and prevision of the disease.
@article{sim2024multi,
author = {Jaeyoon Sim and Minjae Lee and Guorong Wu and Won Hwa Kim},
title = {Multi-Modal Graph Neural Network with Transformer-Guided Adaptive Diffusion for Preclinical Alzheimer Classification},
journal = {MICCAI},
year = {2024}
}
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OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labels

Seunghun Baek*, Jaeyoon Sim*, Guorong Wu, Won Hwa Kim (*: Equal Contribution)

C International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Marrakesh, Morocco, 2024

Provisional Accept [~11% Acceptance Rate]

Project Page

Accurately discriminating progressive stages of Alzheimer’s Disease (AD) is crucial for early diagnosis and prevention. It often involves multiple imaging modalities to understand the complex pathology of AD, however, acquiring a complete set of images is challenging due to high cost and burden for subjects. In the end, missing data become inevitable which lead to limited sample-size and decrease in precision in downstream analyses. To tackle this challenge, we introduce a holistic imaging feature imputation method that enables to leverage diverse imaging features while retaining all subjects. The proposed method comprises two networks: 1) An encoder to extract modality-independent embeddings and 2) A decoder to reconstruct the original measures conditioned on their imaging modalities. The encoder includes a novel ordinal contrastive loss, which aligns samples in the embedding space according to the progression of AD. We also maximize modality-wise coherence of embeddings within each subject, in conjunction with domain adversarial training algorithms, to further enhance alignment between different imaging modalities. The proposed method promotes our holistic imaging feature imputation across various modalities in the shared embedding space. In the experiments, we show that our networks deliver favorable results for statistical analysis and classification against imputation baselines with Alzheimer’s Disease Neuroimaging Initiative (ADNI) study.
@article{baek2024ocl,
author = {Seunghun Baek and Jaeyoon Sim and Guorong Wu and Won Hwa Kim},
title = {OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labels},
journal = {MICCAI},
year = {2024}
}
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Neurodegenerative Brain Network Classification via Adaptive Diffusion with Temporal Regularization

Hyuna Cho, Jaeyoon Sim, Guorong Wu, Won Hwa Kim

C International Conference on Machine Learning (ICML), Vienna, Austria, 2024

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Analysis of neurodegenerative diseases on brain connectomes is important in facilitating early diagnosis and predicting its onset. However, investigation of the progressive and irreversible dynamics of these diseases remains underexplored in cross-sectional studies as its diagnostic groups are considered independent. Also, as in many real-world graphs, brain networks exhibit intricate structures with both homophily and heterophily. To address these challenges, we propose Adaptive Graph diffusion network with Temporal regularization (AGT). AGT introduces node-wise convolution to adaptively capture low (i.e., homophily) and high-frequency (i.e., heterophily) characteristics within an optimally tailored range for each node. Moreover, AGT captures sequential variations within progressive diagnostic groups with a novel temporal regularization, considering the relative feature distance between the groups in the latent space. As a result, our proposed model yields interpretable results at both node-level and group-level. The superiority of our method is validated on two neurodegenerative disease benchmarks for graph classification: Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Parkinson’s Progression Markers Initiative (PPMI) datasets.
@inproceedings{
cho2024neurodegenerative,
title={Neurodegenerative Brain Network Classification via Adaptive Diffusion with Temporal Regularization},
author={Hyuna Cho and Jaeyoon Sim and Guorong Wu and Won Hwa Kim},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=GTnn6bNE3j}
}
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Modality-Agnostic Style Transfer for Holistic Feature Imputation

Seunghun Baek*, Jaeyoon Sim*, Mustafa Dere, Minjeong Kim, Guorong Wu, Won Hwa Kim (*: Equal Contribution)

C International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 2024

Oral Presentation

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Characterizing a preclinical stage of Alzheimer’s Disease (AD) via single imaging is difficult as its early symptoms are quite subtle. Therefore, many neuroimaging studies are curated with various imaging modalities, e.g., MRI and PET, however, it is often challenging to acquire all of them from all subjects and missing data become inevitable. In this regards, in this paper, we propose a framework that generates unobserved imaging measures for specific subjects using their existing measures, thereby reducing the need for additional examinations. Our framework transfers modality-specific style while preserving AD-specific content. This is done by domain adversarial training that preserves modality-agnostic but AD-specific information, while a generative adversarial network adds an indistinguishable modality-specific style. Our proposed framework is evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study and compared with other imputation methods in terms of generated data quality. Small average Cohen’s d < 0.19 between our generated measures and real ones suggests that the synthetic data are practically usable regardless of their modality type.
@INPROCEEDINGS{10635492,
author={Baek, Seunghun and Sim, Jaeyoon and Dere, Mustafa and Kim, Minjeong and Wu, Guorong and Kim, Won Hwa},
booktitle={2024 IEEE International Symposium on Biomedical Imaging (ISBI)},
title={Modality-Agnostic Style Transfer for Holistic Feature Imputation},
year={2024},
volume={},
number={},
pages={1-5},
keywords={Neuroimaging;Training;Magnetic resonance imaging;Imaging;
Estimation;Biomedical measurement;Generative adversarial networks},
doi={10.1109/ISBI56570.2024.10635492}}
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Learning to Approximate Adaptive Kernel Convolution on Graphs

Jaeyoon Sim, Sooyeon Jeon, InJun Choi, Guorong Wu, Won Hwa Kim

C Association for the Advancement of Artificial Intelligence (AAAI), Vancouver, Canada, 2024 [23.75% Acceptance Rate]

W Image Processing and Image Understanding (IPIU), Jeju, Korea, 2024

IPIU Outstanding Paper Award - Bronze Prize

BK21 Outstanding Paper Award - Excellence Prize

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Various Graph Neural Networks (GNNs) have been successful in analyzing data in non-Euclidean spaces, however, they have limitations such as oversmoothing, i.e., information becomes excessively averaged as the number of hidden layers increases. The issue stems from the intrinsic formulation of conventional graph convolution where the nodal features are aggregated from a direct neighborhood per layer across the entire nodes in the graph. As setting different number of hidden layers per node is infeasible, recent works leverage a diffusion kernel to redefine the graph structure and incorporate information from farther nodes. Unfortunately, such approaches suffer from heavy diagonalization of a graph Laplacian or learning a large transform matrix. In this regards, we propose a diffusion learning framework where the range of feature aggregation is controlled by the scale of a diffusion kernel. For efficient computation, we derive closed-form derivatives of approximations of the graph convolution with respect to the scale, so that node-wise range can be adaptively learned.With a downstream classifier, the entire framework is made trainable in an end-to-end manner. Our model is tested on various standard datasets for node-wise classification for the state-of-the-art performance, and it is also validated on a real-world brain network data for graph classifications to demonstrate its practicality for Alzheimer classification.
@inproceedings{sim2024learning,
title={Learning to Approximate Adaptive Kernel Convolution on Graphs},
author={Sim, Jaeyoon and Jeon, Sooyeon and Choi, InJun and Wu, Guorong and Kim, Won Hwa},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={5},
pages={4882--4890},
year={2024}
}
Domestic

AI-Driven BGA Solder Joint Failure Detection of PCB Assembly

Seunghun Baek, Jaeyoon Sim, Siyeon Park, Cheolung Yang, Songyi Jeon, Jongho Song, Won Hwa Kim

C Autumn Annual Conference of the Institute of Electronics and Information Engineers (IEIE), Siheung, Korea, 2023

C : Conference     J : Journal     W : Workshop