Arrow Research search

Author name cluster

Hui Hu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
1 author row

Possible papers

2

JBHI Journal 2026 Journal Article

Deep Learning-Based Vitiligo Activity Evaluation Using Wood's Lamp Imaging: A Clinical Decision Support

  • Zheng Wang
  • Zixuan Nie
  • Hui Hu
  • Kaibin Lin
  • Chong Wang
  • Hongyang Fu
  • Jianglin Zhang

Vitiligo is an autoimmune disorder characterized by heterogeneous and unpredictable depigmentation, which poses substantial challenges for objective disease monitoring and treatment evaluation in routine clinical practice. To address the lack of automated and quantitative follow-up tools, we designed and validated an end-to-end deep learning–based system using Wood's lamp imaging to support lesion localization, longitudinal tracking, and activity assessment. The framework employs Mask R-CNN for automated lesion detection and segmentation, followed by quantitative pigmentation-state analysis and disease activity evaluation using t distributed stochastic neighbor embedding (t SNE) and the Vitiligo Disease Activity (VIDA) score. Disease progression is further characterized through risk, correlation, and survival analyses. Quantitatively, the Mask R CNN model achieved robust performance, with a Dice coefficient of 90. 5%, a mean intersection over-union of 83. 1%, and an area under the curve of 0. 9567 on external validation, supporting reliable lesion delineation under Wood's lamp imaging. When benchmarked against representative segmentation backbones (U-Net, U-Net++, U2-Net, and DeepLabV3), the Mask R CNN–based pipeline yielded the highest mean IoU (83. 1 ± 3. 9%) while maintaining a competitive Dice score (90. 5 ± 1. 38%). t-SNE analysis effectively separated pigmentation states, revealing distinct patterns of hyperpigmentation, hypopigmentation, and apigmentation that were consistent with clinical activity. Longitudinal survival analysis demonstrated that stable lesions derived significant benefit from sustained treatment, whereas active lesions exhibited elevated early risk. Age-stratified analysis identified the highest relapse risk in patients aged 11–20 years, while patients aged 71–80 years showed reduced disease stability. Regression analysis further indicated that, during the stable phase, increased associated with hypopigmentation subsequent was associated with subsequent pigment regeneration, underscoring stage-dependent treatment effects. Integrating deep learning based lesion analysis with clinically grounded activity and longitudinal risk modeling enables precise, objective monitoring of vitiligo and supports personalized, stage-aware treatment decision-making.

Details DOI

AAAI Conference 2026 Conference Paper

ToC: Tree-of-Claims Search with Multi-Agent Language Models

  • Shuyang Yu
  • Jianan Liang
  • Hui Hu

Optimizing patent claims is a critical yet challenging task, demanding careful balance between maximizing novelty and preserving legal scope. Manual claim drafting is labor-intensive, costly, and inherently inconsistent, while conventional Large Language Models (LLMs) often lack the structured, iterative reasoning essential for precise claim refinement. To address these challenges, we introduce Tree of Claims (ToC), an innovative framework that redefines claim editing as a guided search problem. ToC synergistically integrates Monte Carlo Tree Search (MCTS) with a collaborative multi-agent system, comprising an LLM-based EditorAgent that proposes contextually grounded edits, and an ExaminerAgent that mimics patent examiner critiques through structured, chain-of-thought analyses of novelty and prior art disclosure. Driven by a carefully designed multi-objective reward function, ToC jointly optimizes novelty, scope retention, and semantic coherence. Experimental evaluation on a benchmark of 1145 claims demonstrates that ToC significantly outperforms standard LLMs in zero-shot and few-shot scenarios, achieving an average composite score improvement of 8%, and up to 9% in certain cases. Extensive experiments, including detailed ablation studies, validate ToC’s efficacy in generating superior, legally robust claim revisions. Overall, ToC establishes a transparent, controllable, and interpretable methodology that effectively bridges advanced LLM reasoning capabilities with strategic MCTS planning for structured patent claim optimization.

PDF Details DOI
v2026.09.13