arXiv subjects

Trung-Hieu Do

Publications and source records attributed to Trung-Hieu Do.

4 recordsLinked to original sources

arXiv2026

A Scientific Claim Stability Framework for Evaluating MRI Morphometry Pipelines

MRI morphometry pipelines are often evaluated by measurement agreement or downstream accuracy, yet their scientific value depends on the conclusions they support. Two pipelines can produce similar feature-level results yet support different conclusions about aging, cognitive decline, or neurodegeneration. We introduce Scientific Claim Stability (SCS), a claim-centric evaluation framework that converts region-of-interest (ROI) morphometry tables into auditable claim cards and evaluates their support under changes in dataset, processing pipeline, atlas, feature family, and target. Across 10,370 MRI scans from three datasets and five cohorts, SCS generates 2,783 claim cards and evaluates 89 claim-family pairs. The evaluation distinguishes stable, transferable, and fragile claims. FreeSurfer 8-FastSurfer comparisons show high evidence concordance and low anatomical-support overlap under the primary covariate specification. Acquisition-adjusted analyses show that the magnitude of support disagreement depends on the model specification. Structured review by specialists in geriatrics, neuroscience, and radiology, alongside auxiliary ChatGPT and Gemini reviewers, assesses claim plausibility, clarity, faithfulness, usefulness, and caution. SCS provides a reproducible way to audit the evidential stability of morphometry claims without assuming a universal gold standard.

cs.LG↗

arXiv2026

MemoCare: An Interactive Multimodal Mobile System for Automated Cognitive Screening

MemoCare is a React Native mobile system for automated multimodal cognitive screening in English and Vietnamese. It integrates speech, temporal and spatial orientation, touchscreen interaction, and visuoconstruction. Google Speech-to-Text transcripts are scored with deterministic, task-specific rules. GPS-grounded current and historical administrative names support location scoring, touch events verify actions, and lightweight convolutional neural networks evaluate drawings. A separate model provides visual interpretation. Software verification passed 151/151 predefined language, spatial-answer, and touch-scoring regression cases and 48/48 fixed-coordinate spatial tests across four countries. Transcript-based tests exclude speech recognition accuracy. On a locked 71-image test set, validation-selected ShuffleNetV2 x1.5 achieved 91.33% mean balanced accuracy and 78.87% exact three-criterion accuracy. Four clinician co-authors rated the workflow (pooled median 4/5 across eight criteria; item medians 3-4.5), highlighting improvements needed in speech handling, response acceptance, and result transparency. The workflow automatically produces item-level and total scores.

cs.MM↗

arXiv2026

KidRisk: Benchmark Dataset for Children Dangerous Action Recognition

Children are naturally energetic, and during their spontaneous activities, they often encounter potentially dangerous situations, especially when lacking parental supervision. Identifying actions that pose risks plays a crucial role in ensuring their safety. This paper build a novel challenging dataset, namely KidRisk, including 2,500 short videos of children's actions and 10,000 images for dangerous action of children. We also introduce a benchmark on our newly constructs dataset and find that traditional deep learning models demonstrated limited effectiveness on these tasks. Therefore, we develop vision-language based baselines with exceptional context understanding of visual information. Our proposed methods achieved an accuracy of 83.53% in classifying children's actions and 96.14% in recognizing children's dangerous actions, significantly outperforming traditional approaches. These results confirm that vision-language models are not only feasible but also highly effective in detecting hazardous actions, contributing positively to safeguarding children's safety.

cs.CV↗

arXiv2025

MUDI: A Multimodal Biomedical Dataset for Understanding Pharmacodynamic Drug-Drug Interactions

Understanding the interaction between different drugs (drug-drug interaction or DDI) is critical for ensuring patient safety and optimizing therapeutic outcomes. Existing DDI datasets primarily focus on textual information, overlooking multimodal data that reflect complex drug mechanisms. In this paper, we (1) introduce MUDI, a large-scale Multimodal biomedical dataset for Understanding pharmacodynamic Drug-drug Interactions, and (2) benchmark learning methods to study it. In brief, MUDI provides a comprehensive multimodal representation of drugs by combining pharmacological text, chemical formulas, molecular structure graphs, and images across 310,532 annotated drug pairs labeled as Synergism, Antagonism, or New Effect. Crucially, to effectively evaluate machine-learning based generalization, MUDI consists of unseen drug pairs in the test set. We evaluate benchmark models using both late fusion voting and intermediate fusion strategies. All data, annotations, evaluation scripts, and baselines are released under an open research license.

cs.LG↗