Author name cluster

Yuxuan Yang

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

MedReasoner: Reinforcement Learning Drives Reasoning Grounding from Clinical Thought to Pixel-Level Precision

Zhonghao Yan
Muxi Diao
Yuxuan Yang
Ruoyan Jing
Jiayuan Xu
Kaizhou Zhang
Lele Yang
Yanxi Liu

Accurately grounding regions of interest (ROIs) is critical for diagnosis and treatment planning in medical imaging. While multimodal large language models (MLLMs) combine visual perception with natural language, current medical-grounding pipelines still rely on supervised fine-tuning with explicit spatial hints, making them ill-equipped to handle the implicit queries common in clinical practice. This work makes three core contributions. We first define Unified Medical Reasoning Grounding (UMRG), a novel vision–language task that demands clinical reasoning and pixel-level grounding. Second, we release U-MRG-14K, a dataset of 14K samples featuring pixel-level masks alongside implicit clinical queries and reasoning traces, spanning 10 modalities, 15 super-categories, and 108 specific categories. Finally, we introduce MedReasoner, a modular framework that distinctly separates reasoning from segmentation: an MLLM reasoner is optimized with reinforcement learning, while a frozen segmentation expert converts spatial prompts into masks, with alignment achieved through format and accuracy rewards. MedReasoner achieves state-of-the-art performance on U-MRG-14K and demonstrates strong generalization to unseen clinical queries, underscoring the significant promise of reinforcement learning for interpretable medical grounding.

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JBHI Journal 2025 Journal Article

Application and Challenges of Deep Learning in Pulmonary Vessels Segmentation of CTPA Images

Yujie Shi
Xianzi Meng
Xinwei Tang
Chen Zhong
Yuxuan Yang
Dongling Guo
Yong Guo
Jianfeng Wang

Accurate segmentation of pulmonary vessels in medical imaging is critical for the diagnosis of pulmonary vascular diseases (PVDs), particularly in conditions such as chronic thromboembolic pulmonary hypertension (CTEPH), which require detailed vascular mapping. This comprehensive review explores recent advancements in deep learning (DL)-based segmentation techniques for computed tomography pulmonary angiography (CTPA) images, focusing on three primary objectives: (1) to systematically classify network architectures by data dimensionality (2D/3D/2. 5D) and assess their clinical adaptability across varying imaging conditions; (2) to perform quantitative performance comparisons using the standardized Dice Similarity Coefficient (DSC); and (3) to address critical challenges in clinical implementation, including annotation scarcity, computational efficiency versus resolution trade-offs, and model generalization limitations, while proposing innovative mitigation strategies. To this end, we adopted the PRISMA methodology, conducting rigorous searches across Google Scholar and IEEE Xplore databases up to 2024, with manual curation identifying 23 high-quality studies for inclusion. In addition to synthesizing current technological limitations, this review highlights emerging directions such as self-supervised learning and domain adaptation, offering clinicians and AI researchers a structured reference that bridges technical innovation with practical clinical needs

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IROS Conference 2025 Conference Paper

Lywal-X: A Novel Wheel-claw Quadruped Robot

Hao Shen
Yuxuan Yang
Yiliang Wang
Xintian Zuo
Hongwei Zhu
Jianming Wang
Xuan Xiao

This paper introduces a wheel-claw quadruped robot named Lywal-X, which is capable of omnidirectional movement as well as grasping actions. Firstly, the mechanical structure of Lywal-X is designed with a three-degree-of-freedom leg transformation mechanism and a two-degree-of-freedom wheel-claw structure. Then, movement strategies for different modes such as climbing and grasping are developed. Finally, the mobility performance of Lywal-X is analyzed, and physical experiments are conducted to verify the robot’s ability to pick up and transport target objects in both single-claw and double-claw modes.

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JBHI Journal 2025 Journal Article

MSMTSeg: Multi-Stained Multi-Tissue Segmentation of Kidney Histology Images via Generative Self-Supervised Meta-Learning Framework

Xueyu Liu
Rui Wang
Yexin Lai
Yongfei Wu
Hangbei Cheng
Yuanyue Lu
Jianan Zhang
Ning Hao

Accurately diagnosing chronic kidney disease requires pathologists to assess the structure of multiple tissues under different stains, a process that is time-consuming and labor-intensive. Current AI-based methods for automatic structure assessment, like segmentation, often demand extensive manual annotation and focus on single stain domain. To address these challenges, we introduce MSMTSeg, a generative self-supervised meta-learning framework for multi-stained multi-tissue segmentation in renal biopsy whole slide images (WSIs). MSMTSeg incorporates multiple stain transform models for style translation of inter-stain domains, a self-supervision module for obtaining pre-trained models with the domain-specific feature representation, and a meta-learning strategy that leverages generated virtual data and pre-trained models to learn the domain-invariant feature representation across multiple stains, thereby enhancing segmentation performance. Experimental results demonstrate that MSMTSeg achieves superior and robust performance, with mDSC of 0. 836 and mIoU of 0. 718 for multiple tissues under different stains, using only one annotated training sample for each stain. Our ablation study confirms the effectiveness of each component, positioning MSMTSeg ahead of classic advanced segmentation networks, recent few-shot segmentation methods, and unsupervised domain adaptation methods. In conclusion, our proposed few-shot cross-domain technology offers a feasible and cost-effective solution for multi-stained renal histology segmentation, providing convenient assistance to pathologists in clinical practice.

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NeurIPS Conference 2025 Conference Paper

Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting

Yuxuan Yang
Dalin Zhang
Yuxuan Liang
Hua Lu
Gang Chen
Huan Li

Time Series Forecasting (TSF) is a crucial task in various domains, yet existing TSF models rely heavily on high-quality data and insufficiently exploit all available data. This paper explores a novel self-supervised approach to re-label time series datasets by inherently constructing candidate datasets. During the optimization of a simple reconstruction network, intermediates are used as pseudo labels in a self-supervised paradigm, improving generalization for any predictor. We introduce the Self-Correction with Adaptive Mask (SCAM), which discards overfitted components and selectively replaces them with pseudo labels generated from reconstructions. Additionally, we incorporate Spectral Norm Regularization (SNR) to further suppress overfitting from a loss landscape perspective. Our experiments on eleven real-world datasets demonstrate that SCAM consistently improves the performance of various backbone models. This work offers a new perspective on constructing datasets and enhancing the generalization of TSF models through self-supervised learning. The code is available at https: //github. com/SuDIS-ZJU/SCAM.

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AAAI Conference 2023 Short Paper

Cross-Regional Fraud Detection via Continual Learning (Student Abstract)

Yujie Li
Yuxuan Yang
Qiang Gao
Xin Yang

Detecting fraud is an urgent task to avoid transaction risks. Especially when expanding a business to new cities or new countries, developing a totally new model will bring the cost issue and result in forgetting previous knowledge. This study proposes a novel solution based on heterogeneous trade graphs, namely HTG-CFD, to prevent knowledge forgetting of cross-regional fraud detection. Specifically, a novel heterogeneous trade graph is meticulously constructed from original transactions to explore the complex semantics among different types of entities and relationships. Motivated by continual learning, we present a practical and task-oriented forgetting prevention method to alleviate knowledge forgetting in the context of cross-regional detection. Extensive experiments demonstrate that HTG-CFD promotes performance in both cross-regional and single-regional scenarios.

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