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Yixuan Wu

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8 papers

2 author rows

JBHI Journal 2026 Journal Article

RetinexDA: Progressive Disentanglement Domain Adaptation for Unsupervised Cross-Modality Medical Image Segmentation

Yixuan Wu
Mingze Yin
Zitai Kong
Jintai Chen
Jian Wu
Honghao Gao
Hongxia Xu

Deep neural networks have achieved strong performance in medical image segmentation when the training and testing data share similar appearance characteristics. However, this assumption is rarely satisfied in practical clinical scenarios, where imaging protocols, scanner vendors, and modality physics differ substantially, resulting in severe performance degradation when the model is deployed to new environments. To address this challenge, we propose RetinexDA, a novel unsupervised domain adaptation framework that explicitly decomposes a medical image into domain-invariant structural and domain-specific appearance representations. This Retinex-inspired formulation preserves essential anatomical details while mitigating modality-dependent variations. Furthermore, we introduce Disentangled Knowledge Distillation (DKD) to ensure mutual semantic alignment between the structure–appearance decomposition in pixel space and the encoded features in latent space, strengthening fine-grained segmentation capability. In addition, a Bézier-curve domain bridging strategy is developed to generate smoothly transitioned intermediate samples across domains, improving adaptation robustness under large modality discrepancies. Extensive experiments on abdominal CT and cardiac MRI segmentation tasks demonstrate that RetinexDA surpasses state-of-the-art unsupervised domain adaptation approaches, showing strong potential for scalable and reliable clinical deployment.

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JMLR Journal 2025 Journal Article

Assumption-lean and data-adaptive post-prediction inference

Jiacheng Miao
Xinran Miao
Yixuan Wu
Jiwei Zhao
Qiongshi Lu

A primary challenge facing modern scientific research is the limited availability of gold-standard data, which can be costly, labor-intensive, or invasive to obtain. With the rapid development of machine learning (ML), scientists can now employ ML algorithms to predict gold-standard outcomes using variables that are easier to obtain. However, these predicted outcomes are often used directly in subsequent statistical analyses, ignoring imprecision and heterogeneity introduced by the prediction procedure. This will likely result in false positive findings and invalid scientific conclusions. In this work, we introduce PoSt-Prediction Adaptive inference (PSPA) that allows valid and powerful inference based on ML-predicted data. Its “assumption-lean” property guarantees reliable statistical inference without assumptions on the ML prediction. Its “data-adaptive” feature guarantees an efficiency gain over existing methods, regardless of the accuracy of ML prediction. We demonstrate the statistical superiority and broad applicability of our method through simulations and real-data applications. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2025. ( edit, beta )

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ICML Conference 2025 Conference Paper

Minimalist Concept Erasure in Generative Models

Yang Zhang
Er Jin
Yanfei Dong
Yixuan Wu
Philip H. S. Torr
Ashkan Khakzar
Johannes Stegmaier
Kenji Kawaguchi

Recent advances in generative models have demonstrated remarkable capabilities in producing high-quality images, but their reliance on large-scale unlabeled data has raised significant safety and copyright concerns. Efforts to address these issues by erasing unwanted concepts have shown promise. However, many existing erasure methods involve excessive modifications that compromise the overall utility of the model. In this work, we address these issues by formulating a novel minimalist concept erasure objective based only on the distributional distance of final generation outputs. Building on our formulation, we derive a tractable loss for differentiable optimization that leverages backpropagation through all generation steps in an end-to-end manner. We also conduct extensive analysis to show theoretical connections with other models and methods. To improve the robustness of the erasure, we incorporate neuron masking as an alternative to model fine-tuning. Empirical evaluations on state-of-the-art flow-matching models demonstrate that our method robustly erases concepts without degrading overall model performance, paving the way for safer and more responsible generative models.

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IJCAI Conference 2024 Conference Paper

AI-Enhanced Virtual Reality in Medicine: A Comprehensive Survey

Yixuan Wu
Kaiyuan Hu
Danny Z. Chen
Jian Wu

With the rapid advance of computer graphics and artificial intelligence technologies, the ways we interact with the world have undergone a transformative shift. Virtual Reality (VR) technology, aided by artificial intelligence (AI), has emerged as a dominant interaction media in multiple application areas, thanks to its advantage of providing users with immersive experiences. Among those applications, medicine is considered one of the most promising areas. In this paper, we present a comprehensive examination of the burgeoning field of AI-enhanced VR applications in medical care and services. By introducing a systematic taxonomy, we meticulously classify the pertinent techniques and applications into three well-defined categories based on different phases of medical diagnosis and treatment: Visualization Enhancement, VR-related Medical Data Processing, and VR-assisted Intervention. This categorization enables a structured exploration of the diverse roles that AI-powered VR plays in the medical domain, providing a framework for a more comprehensive understanding and evaluation of these technologies. nTo our best knowledge, this work is the first systematic survey of AI-powered VR systems in medical settings, laying a foundation for future research in this interdisciplinary domain.

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NeurIPS Conference 2023 Conference Paper

Described Object Detection: Liberating Object Detection with Flexible Expressions

Chi Xie
Zhao Zhang
Yixuan Wu
Feng Zhu
Rui Zhao
Shuang Liang

Detecting objects based on language information is a popular task that includes Open-Vocabulary object Detection (OVD) and Referring Expression Comprehension (REC). In this paper, we advance them to a more practical setting called *Described Object Detection* (DOD) by expanding category names to flexible language expressions for OVD and overcoming the limitation of REC only grounding the pre-existing object. We establish the research foundation for DOD by constructing a *Description Detection Dataset* ($D^3$). This dataset features flexible language expressions, whether short category names or long descriptions, and annotating all described objects on all images without omission. By evaluating previous SOTA methods on $D^3$, we find some troublemakers that fail current REC, OVD, and bi-functional methods. REC methods struggle with confidence scores, rejecting negative instances, and multi-target scenarios, while OVD methods face constraints with long and complex descriptions. Recent bi-functional methods also do not work well on DOD due to their separated training procedures and inference strategies for REC and OVD tasks. Building upon the aforementioned findings, we propose a baseline that largely improves REC methods by reconstructing the training data and introducing a binary classification sub-task, outperforming existing methods. Data and code are available at https: //github. com/shikras/d-cube and related works are tracked in https: //github. com/Charles-Xie/awesome-described-object-detection.

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IROS Conference 2023 Conference Paper

Gaussian Max-Value Entropy Search for Multi-Agent Bayesian Optimization

Haitong Ma
Tianpeng Zhang
Yixuan Wu
Flávio P. Calmon
Na Li 0002

We study the multi-agent Bayesian optimization (BO) problem, where multiple agents maximize a black-box function via iterative queries. We focus on Entropy Search (ES), a sample-efficient BO algorithm that selects queries to maximize the mutual information about the maximum of the black-box function. One of the main challenges of ES is that calculating the mutual information requires computationallycostly approximation techniques. For multi-agent BO problems, the computational cost of ES is exponential in the number of agents. To address this challenge, we propose the Gaussian Max-value Entropy Search, a multi-agent BO algorithm with favorable sample and computational efficiency. The key to our idea is to use a normal distribution to approximate the function maximum and calculate its mutual information accordingly. The resulting approximation allows queries to be cast as the solution of a closed-form optimization problem which, in turn, can be solved via a modified gradient ascent algorithm and scaled to a large number of agents. We demonstrate the effectiveness of Gaussian max-value Entropy Search through numerical experiments on standard test functions and real-robot experiments on the source seeking problem. Results show that the proposed algorithm outperforms the multi-agent BO baselines in the numerical experiments and can stably seek the source with a limited number of noisy observations on real robots.

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IJCAI Conference 2023 Conference Paper

MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation

Yiheng Zhu
Zhenqiu Ouyang
Ben Liao
Jialu Wu
Yixuan Wu
Chang-Yu Hsieh
Tingjun Hou
Jian Wu

Molecular de novo design is a critical yet challenging task in scientific fields, aiming to design novel molecular structures with desired property profiles. Significant progress has been made by resorting to generative models for graphs. However, limited attention is paid to hierarchical generative models, which can exploit the inherent hierarchical structure (with rich semantic information) of the molecular graphs and generate complex molecules of larger size that we shall demonstrate to be difficult for most existing models. The primary challenge to hierarchical generation is the non-differentiable issue caused by the generation of intermediate discrete coarsened graph structures. To sidestep this issue, we cast the tricky hierarchical generation problem over discrete spaces as the reverse process of hierarchical representation learning and propose MolHF, a new hierarchical flow-based model that generates molecular graphs in a coarse-to-fine manner. Specifically, MolHF first generates bonds through a multi-scale architecture, then generates atoms based on the coarsened graph structure at each scale. We demonstrate that MolHF achieves state-of-the-art performance in random generation and property optimization, implying its high capacity to model data distribution. Furthermore, MolHF is the first flow-based model that can be applied to model larger molecules (polymer) with more than 100 heavy atoms. The code and models are available at https: //github. com/violet-sto/MolHF.

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AAAI Conference 2023 Conference Paper

T2G-FORMER: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction

Jiahuan Yan
Jintai Chen
Yixuan Wu
Danny Z. Chen
Jian Wu

Recent development of deep neural networks (DNNs) for tabular learning has largely benefited from the capability of DNNs for automatic feature interaction. However, the heterogeneity nature of tabular features makes such features relatively independent, and developing effective methods to promote tabular feature interaction still remains an open problem. In this paper, we propose a novel Graph Estimator, which automatically estimates the relations among tabular features and builds graphs by assigning edges between related features. Such relation graphs organize independent tabular features into a kind of graph data such that interaction of nodes (tabular features) can be conducted in an orderly fashion. Based on our proposed Graph Estimator, we present a bespoke Transformer network tailored for tabular learning, called T2G-Former, which processes tabular data by performing tabular feature interaction guided by the relation graphs. A specific Cross-level Readout collects salient features predicted by the layers in T2G-Former across different levels, and attains global semantics for final prediction. Comprehensive experiments show that our T2G-Former achieves superior performance among DNNs and is competitive with non-deep Gradient Boosted Decision Tree models. The code and detailed results are available at https://github.com/jyansir/t2g-former.

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