no code implementations • 13 Feb 2024 • Kwanseok Oh, Jieun Lee, Da-Woon Heo, Dinggang Shen, Heung-Il Suk
Specifically, our adaptive fusion module aggregates 7T-like features derived from the LF image by a pre-trained network and then refines them to be effectively assimilable UHF guidance into LF image features.
no code implementations • 23 Oct 2023 • Ahmad Wisnu Mulyadi, Heung-Il Suk
Extensive adoption of electronic health records (EHRs) offers opportunities for its use in various clinical analyses.
no code implementations • 6 Oct 2023 • Eunsong Kang, Da-Woon Heo, Jiwon Lee, Heung-Il Suk
Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately.
no code implementations • 5 Oct 2023 • Kwanseok Oh, Da-Woon Heo, Ahmad Wisnu Mulyadi, Wonsik Jung, Eunsong Kang, Kun Ho Lee, Heung-Il Suk
Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention in disease progression yet still demands attentive interpretability to explain how their DL models make definitive decisions.
no code implementations • 5 Oct 2023 • Seungwoo Jeong, Wonsik Jung, Junghyo Sohn, Heung-Il Suk
We verify our proposed method's efficacy by predicting clinical labels and cognitive scores over time in regular and irregular settings.
no code implementations • 5 Oct 2023 • Jee Seok Yoon, Kwanseok Oh, Yooseung Shin, Maciej A. Mazurowski, Heung-Il Suk
Medical image analysis (MedIA) has become an essential tool in medicine and healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in deep learning (DL) have made significant contributions to its advances.
1 code implementation • 5 Oct 2023 • Wonsik Jung, Eunjin Jeon, Eunsong Kang, Heung-Il Suk
Deep learning models based on resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used to diagnose brain diseases, particularly autism spectrum disorder (ASD).
1 code implementation • 16 Dec 2022 • Jee Seok Yoon, Chenghao Zhang, Heung-Il Suk, Jia Guo, Xiaoxiao Li
To this end, we propose a sequence-aware diffusion model (SADM) for the generation of longitudinal medical images.
1 code implementation • 27 Jul 2022 • Ahmad Wisnu Mulyadi, Wonsik Jung, Kwanseok Oh, Jee Seok Yoon, Heung-Il Suk
By considering this pseudo map as an enriched reference, we employ an estimating network to estimate the AD likelihood map over a 3D sMRI scan.
1 code implementation • 22 Mar 2022 • Jauen Phyo, Wonjun Ko, Eunjin Jeon, Heung-Il Suk
Based on our overall results, we believe that TransSleep has immense potential to provide new insights into deep learning-based sleep staging.
1 code implementation • 3 Dec 2021 • Seungwoo Jeong, Wonjun Ko, Ahmad Wisnu Mulyadi, Heung-Il Suk
Modeling non-Euclidean data is drawing extensive attention along with the unprecedented successes of deep neural networks in diverse fields.
1 code implementation • 21 Aug 2021 • Kwanseok Oh, Jee Seok Yoon, Heung-Il Suk
Existing studies on disease diagnostic models focus either on diagnostic model learning for performance improvement or on the visual explanation of a trained diagnostic model.
2 code implementations • 10 Aug 2021 • Changhyun Park, Heung-Il Suk
Various data-driven approaches have been introduced to capture subtle and local morphological changes of the brain accompanied by the disease progression.
no code implementations • 28 Apr 2021 • Eunji Jun, Seungwoo Jeong, Da-Woon Heo, Heung-Il Suk
For building a source model generally applicable to various tasks, we pre-train the model in a self-supervised learning manner for masked encoding vector prediction as a proxy task, using a large-scale normal, healthy brain magnetic resonance imaging (MRI) dataset.
no code implementations • 11 Apr 2021 • Junghyo Sohn, Eunjin Jeon, Wonsik Jung, Eunsong Kang, Heung-Il Suk
Although recent advances in deep learning accelerated an improvement in a weakly supervised object localization (WSOL) task, there are still challenges to identify the entire body of an object, rather than only discriminative parts.
no code implementations • 25 Jan 2021 • Yurim Lee, Eunji Jun, Heung-Il Suk
In addition, we build an attention-based decoder as a missing value imputer that helps empower the representation learning of the inter-relations among multi-view observations for the prediction task, which operates at the training phase only.
1 code implementation • 20 Nov 2020 • Kwanseok Oh, Jee Seok Yoon, Heung-Il Suk
Specifically, our proposed BIN consists of two core components: Counterfactual Map Generator and Target Attribution Network.
no code implementations • 1 Jul 2020 • Wonjun Ko, Eunjin Jeon, Heung-Il Suk
In this work, we formulate the problem of estimating and selecting task-relevant temporal signal segments from a single EEG trial in the form of a Markov decision process and propose a novel reinforcement-learning mechanism that can be combined with the existing deep-learning based BCI methods.
1 code implementation • 6 May 2020 • Wonsik Jung, Eunji Jun, Heung-Il Suk
While many of the previous works considered cross-sectional analysis, more recent studies have focused on the diagnosis and prognosis of AD with longitudinal or time series data in a way of disease progression modeling (DPM).
no code implementations • 2 Mar 2020 • Wonjun Ko, Eunjin Jeon, Seungwoo Jeong, Heung-Il Suk
Among the various deep network architectures, convolutional neural networks have been well suited for spatio-spectral-temporal electroencephalogram signal representation learning.
1 code implementation • 2 Mar 2020 • Ahmad Wisnu Mulyadi, Eunji Jun, Heung-Il Suk
In this work, we propose a novel variational-recurrent imputation network, which unifies an imputation and a prediction network by taking into account the correlated features, temporal dynamics, as well as the uncertainty.
1 code implementation • 2 Mar 2020 • Eunji Jun, Ahmad Wisnu Mulyadi, Jaehun Choi, Heung-Il Suk
However, once the missing values are imputed, most existing methods do not consider the fidelity or confidence of the imputed values in the modeling of downstream tasks.
1 code implementation • 17 Oct 2019 • Eunjin Jeon, Wonjun Ko, Jee Seok Yoon, Heung-Il Suk
In this paper, we propose a novel framework that learns class-relevant and subject-invariant feature representations in an information-theoretic manner, without using adversarial learning.
2 code implementations • 27 May 2019 • Jee Seok Yoon, Myung-Cheol Roh, Heung-Il Suk
In this article, we focus on decomposing latent representations in generative adversarial networks or learned feature representations in deep autoencoders into semantically controllable factors in a semisupervised manner, without modifying the original trained models.
1 code implementation • 24 Jul 2018 • Bum-Chae Kim, Jun-Sik Choi, Heung-Il Suk
Lung cancer is a global and dangerous disease, and its early detection is crucial to reducing the risks of mortality.
no code implementations • CVPR 2014 • Xiaofeng Zhu, Heung-Il Suk, Dinggang Shen
We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the newly devised loss function was effective to enhance the performances of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.
no code implementations • CVPR 2014 • Yinghuan Shi, Heung-Il Suk, Yang Gao, Dinggang Shen
Therefore, it is natural to hypothesize that the low-level features extracted from neuroimaging data are related to each other in some ways.