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Minwoo Cho

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4 papers

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YNIMG Journal 2026 Journal Article

BTS-Net: Barlow twins-based superresolution for 7T human brain MRI

Youho Myong
Dan Yoon
Young Gyun Kim
Yongsik Sim
Jee-Hyun Cho
Youngkyu Song
Minwoo Cho
Byung-Mo Oh

This study aimed to develop and validate a Barlow Twins-based superresolution diffusion network (BTS-Net) for 7T human brain magnetic resonance imaging (MRI) superresolution (SR). A paired 3T-7T human brain MRI database was constructed from 50 healthy adult participants (26 females; 52.0%; age range: 27-68 years; median age: 38 years), with anonymized scans. To develop BTS-Net, we employed Barlow Twins, a self-supervised learning (SSL) method, in a latent diffusion model (LDM) to enhance feature representation learning for SR from 3T MRI to 7T-like (BTS-7T) MRI. The image quality of the 3T, 7T, LDM-7T, and BTS-7T MRI was evaluated using the peak signal-to-noise ratio, structural similarity index measure, and normalized root mean squared error. The paired t-test was used to evaluate the mean difference between each imaging group. The three-dimensional structural fidelity was evaluated in 14 anatomical regions (the bilateral thalamus, caudate, putamen, globus pallidus, hippocampus, amygdala, and nucleus accumbens) using voxel-based morphometry. An external dataset consisting of 10 healthy participants was used to validate BTS-Net by performing identical SR, image quality, and volumetric analyses. In both the in-house and external validation datasets, BTS-7T MRI exhibited superior image quality across all three metrics compared to 3T MRI. Ground-truth-based error maps showed that BTS-7T images displayed qualitatively improved anatomical fidelity compared to 3T images. There were no statistically significant differences in volumetry between 10 (in-house) and 11 (validation) of the 14 anatomical regions. The right hippocampus, putamen, and amygdala volumes showed significantly higher agreement in BTS-7T images of the in-house dataset; whereas the bilateral putamen, right thalamus and amygdala volumes showed significantly higher agreement in the validation dataset. This study highlights the potential of BTS-Net to enhance both qualitative visualization and quantitative analysis of 3T brain MRI, with possible applicability to early neurodegenerative conditions characterized by subtle morphological changes. Therefore, additional research using larger patient datasets could aid the possible adoption of this technology in clinical settings.

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AAMAS Conference 2026 Conference Paper

Graph-Conditioned Diffusion for Offline Multi-Agent Reinforcement Learning

Luis Pimentel
Minwoo Cho
Sean Ye
James Ellis Grant Pagan
Matthew Gombolay

Multi-agent reinforcement learning struggles with scalability and real-world applicability, as the high interaction variability across team compositions limits the effectiveness of online adaptive methods. Alternatively, offline RL can address these limitations by leveraging diverse offline data to facilitate learning across teams. However, existingofflineRLmethodsfailtoproducemulti-agentpolicies that can both adapt using only offline data and coordinate effectively under decentralized execution. To address these challenges, we present Graph Conditioned Diffusion (GCD), a multi-agent diffusion framework that uses graph-based communication to learn generalizable offline policies and maintain decentralization during execution. Our framework leverages the conditional generative modeling ability of diffusion models to learn multi-modal distributions of trajectories across team compositions by conditioning on team communication embeddings. We then adapt coordination online through classifier-free guidance, which steers the generative process toward behaviors that generalize across team compositions. We evaluate our method on the StarCraft II Multi-Agent Challenge v2 (SMACv2) domain, demonstrating superior generalization with an average win-rate improvement of 7. 4% to 221. 4% in unseen team compositions compared to decentralized baselines.

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NeurIPS Conference 2025 Conference Paper

Heterogeneous Graph Transformers for Simultaneous Mobile Multi-Robot Task Allocation and Scheduling under Temporal Constraints

Batuhan Altundas
Shengkang Chen
Shivika Singh
Shivangi Deo
Minwoo Cho
Matthew Gombolay

Coordinating large teams of heterogeneous mobile agents to perform complex tasks efficiently has scalability bottlenecks in feasible and optimal task scheduling, with critical applications in logistics, manufacturing, and disaster response. Existing task allocation and scheduling methods, including heuristics and optimization-based solvers, often fail to scale and overlook inter-task dependencies and agent heterogeneity. We propose a novel Simultaneous Decision-Making model for Heterogeneous Multi-Agent Task Allocation and Scheduling (HM-MATAS), built on a Residual Heterogeneous Graph Transformer with edge and node-level attention. Our model encodes agent capabilities, travel times, and temporospatial constraints into a rich graph representation and is trainable via reinforcement learning. Trained on small-scale problems (10 agents, 20 tasks), our model generalizes effectively to significantly larger scenarios (up to 40 agents and 200 tasks), enabling fast, one-shot task assignment and scheduling. Our simultaneous model outperforms classical heuristics by assigning 164. 10\% more feasible tasks given temporal constraints in 3. 83\% of the time, metaheuristics by 201. 54\% in 0. 01\% of the time and exact solver by 231. 73\% in 0. 03\% of the time, while achieving $20\times$-to-$250\times$ speedup from prior graph-based methods across scales.

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YNIMG Journal 2024 Journal Article

Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification

Dan Yoon
Youho Myong
Young Gyun Kim
Yongsik Sim
Minwoo Cho
Byung-Mo Oh
Sungwan Kim

INTRODUCTION: Timely diagnosis and prognostication of Alzheimer's disease (AD) and mild cognitive impairment (MCI) are pivotal for effective intervention. Artificial intelligence (AI) in neuroradiology may aid in such appropriate diagnosis and prognostication. This study aimed to evaluate the potential of novel diffusion model-based AI for enhancing AD and MCI diagnosis through superresolution (SR) of brain magnetic resonance (MR) images. METHODS: 1.5T brain MR scans of patients with AD or MCI and healthy controls (NC) from Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) were superresolved to 3T using a novel diffusion model-based generative AI (d3T*) and a convolutional neural network-based model (c3T*). Comparisons of image quality to actual 1.5T and 3T MRI were conducted based on signal-to-noise ratio (SNR), naturalness image quality evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Voxel-based volumetric analysis was then conducted to study whether 3T* images offered more accurate volumetry than 1.5T images. Binary and multiclass classifications of AD, MCI, and NC were conducted to evaluate whether 3T* images offered superior AD classification performance compared to actual 1.5T MRI. Moreover, CNN-based classifiers were used to predict conversion of MCI to AD, to evaluate the prognostication performance of 3T* images. The classification performances were evaluated using accuracy, sensitivity, specificity, F1 score, Matthews correlation coefficient (MCC), and area under the receiver-operating curves (AUROC). RESULTS: Analysis of variance (ANOVA) detected significant differences in image quality among the 1.5T, c3T*, d3T*, and 3T groups across all metrics. Both c3T* and d3T* showed superior image quality compared to 1.5T MRI in NIQE and BRISQUE with statistical significance. While the hippocampal volumes measured in 3T* and 3T images were not significantly different, the hippocampal volume measured in 1.5T images showed significant difference. 3T*-based AD classifications showed superior performance across all performance metrics compared to 1.5T-based AD classification. Classification performance between d3T* and actual 3T was not significantly different. 3T* images offered superior accuracy in predicting the conversion of MCI to AD than 1.5T images did. CONCLUSIONS: The diffusion model-based MRI SR enhances the resolution of brain MR images, significantly improving diagnostic and prognostic accuracy for AD and MCI. Superresolved 3T* images closely matched actual 3T MRIs in quality and volumetric accuracy, and notably improved the prediction performance of conversion from MCI to AD.

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