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Yiqing Shen

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8 papers

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JBHI Journal 2026 Journal Article

DiffM 4 RI: A Latent Diffusion Model With Modality Inpainting for Synthesizing Missing Modalities in MRI Analysis

Wen Ye
Zhetao Guo
Yuxiang Ren
Yi Tian
Yushi Shen
Zan Chen
Junjun He
Jing Ke

Foundation Models (FMs) have shown great promise for multimodal medical image analysis such as Magnetic Resonance Imaging (MRI). However, certain MRI sequences may be unavailable due to various constraints, such as limited scanning time, patient discomfort, or scanner limitations. The absence of certain modalities can hinder the performance of FMs in clinical applications, making effective missing modality imputation crucial for ensuring their applicability. Previous approaches, including generative adversarial networks (GANs), have been employed to synthesize missing modalities in either a one-to-one or many-to-one manner. However, these methods have limitations, as they require training a new model for different missing scenarios and are prone to mode collapse, generating limited diversity in the synthesized images. To address these challenges, we propose DiffM 4 RI, a diffusion model for many-to-many missing modality imputation in MRI. DiffM 4 RI innovatively formulates the missing modality imputation as a modality-level inpainting task, enabling it to handle arbitrary missing modality situations without the need for training multiple networks. Experiments on the BraTs datasets demonstrate DiffM 4 RI can achieve an average SSIM improvement of 0. 15 over MustGAN, 0. 1 over SynDiff, and 0. 02 over VQ-VAE-2. These results highlight the potential of DiffM 4 RI in enhancing the reliability of FMs in clinical applications.

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AAAI Conference 2026 Conference Paper

Mitigating Hallucinations in Large Language Models via Causal Reasoning

Yuangang Li
Yiqing Shen
Yi Nian
Jiechao Gao
Ziyi Wang
Chenxiao Yu
Li Li
Jie Wang

Large language models (LLMs) exhibit logically inconsistent hallucinations that appear coherent yet violate reasoning principles, with recent research suggesting an inverse relationship between causal reasoning capabilities and such hallucinations. However, existing reasoning approaches in LLMs, such as Chain-of-Thought (CoT) and its graph-based variants, operate at the linguistic token level rather than modeling the underlying causal relationships between variables, lacking the ability to represent conditional independencies or satisfy causal identification assumptions. To bridge this gap, we introduce causal-DAG construction and reasoning (CDCR-SFT), a supervised fine-tuning framework that trains LLMs to explicitly construct variable-level directed acyclic graph (DAG) and then perform reasoning over it. Moreover, we present a dataset comprising 25,368 samples (CausalDR), where each sample includes an input question, explicit causal DAG, graph-based reasoning trace, and validated answer. Experiments on four LLMs across eight tasks show that CDCR-SFT improves the causal reasoning capability with the state-of-the-art 95.33% accuracy on CLADDER (surpassing human performance of 94.8% for the first time) and reduces the hallucination on HaluEval with 10% improvements. It demonstrates that explicit causal structure modeling in LLMs can effectively mitigate logical inconsistencies in LLM outputs.

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AIIM Journal 2025 Journal Article

A survey for large language models in biomedicine

Chong Wang
Mengyao Li
Junjun He
Zhongruo Wang
Erfan Darzi
Zan Chen
Jin Ye
Tianbin Li

Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains. This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of Science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies. Then, we discuss adaptation strategies of LLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to enhance their performance in specialized biomedical contexts where zero-shot fails to achieve, such as medical question answering and efficient processing of biomedical literature. Finally, we discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics due to the sensitive nature of biomedical data, the need for highly reliable model outputs, and the ethical implications of deploying AI in healthcare. To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs. As this field of LLM rapidly evolves, continued research and development are essential to fully harness the capabilities of LLMs in biomedicine while ensuring their responsible and effective deployment.

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NeurIPS Conference 2025 Conference Paper

RBench-V: A Primary Assessment for Visual Reasoning Models with Multimodal Outputs

Meng-Hao Guo
Xuanyu Chu
Qianrui Yang
Zhe-Han Mo
Yiqing Shen
Pei-lin Li
Xinjie Lin
Jinnian Zhang

The rapid advancement of native multi-modal models and omni-models, exemplified by GPT-4o, Gemini and o3 with their capability to process and generate content across modalities such as text and images, marks a significant milestone in the evolution of intelligence. Systematic evaluation of their multi-modal output capabilities in visual thinking process (a. k. a. , multi-modal chain of thought, M-CoT) becomes critically important. However, existing benchmarks for evaluating multi-modal models primarily focus on assessing multi-modal inputs and text-only reasoning process while neglecting the importance of reasoning through multi-modal outputs. In this paper, we present a benchmark, dubbed as RBench-V, designed to assess models’ vision-indispensable reasoning. To conduct RBench-V, we carefully hand-pick 803 questions covering math, physics, counting and games. Unlike problems in previous benchmarks, which typically specify certain input modalities, RBench-V presents problems centered on multi-modal outputs, which require image manipulation, such as generating novel images and constructing auxiliary lines to support reasoning process. We evaluate numerous open- and closed-source models on RBench-V, including o3, Gemini 2. 5 pro, Qwen2. 5-VL, etc. Even the best-performing model, o3, achieves only 25. 8% accuracy on RBench-V, far below the human score of 82. 3%, which shows current models struggle to leverage multi-modal reasoning. Data and code are available at https: //evalmodels. github. io/rbenchv.

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JBHI Journal 2024 Journal Article

RandStainNA++: Enhance Random Stain Augmentation and Normalization Through Foreground and Background Differentiation

Chong Wang
Shuxin Li
Jing Ke
Chen Zhang
Yiqing Shen

The wide prevalence of staining variations in digital pathology presents a significant obstacle, often undermining the effectiveness of diagnosis and analysis. The current strategies to counteract this issue primarily revolve around Stain Normalization (SN) and Stain Augmentation (SA). Nonetheless, these methodologies come with inherent limitations. They struggle to adapt to the vast array of staining styles, tend to presuppose linear associations between color spaces, and often lead to unrealistic color transformations. In response to these challenges, we introduce RandStainNA++, a novel method seamlessly integrating SN and SA. This method exploits the versatility of random SN and SA within randomly selected color spaces, effectively managing variations for the foreground and background independently. By refining the transformations of staining styles for the foreground and background within a realistic scope, this strategy promotes the generation of more practical staining transformations during the training phase. Further enhancing our approach, we propose a unique self-distillation method. This technique incorporates prior knowledge of stain variation, substantially augmenting the generalization capability of the network. The striking results yield that, compared to conventional classification models, our method boosts performance by a significant margin of 16-25%. Furthermore, when juxtaposed with baseline segmentation models, the Dice score registers an increase of 0. 06.

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AIIM Journal 2024 Journal Article

Scalable Swin Transformer network for brain tumor segmentation from incomplete MRI modalities

Dongsong Zhang
Changjian Wang
Tianhua Chen
Weidao Chen
Yiqing Shen

Background: Deep learning methods have shown great potential in processing multi-modal Magnetic Resonance Imaging (MRI) data, enabling improved accuracy in brain tumor segmentation. However, the performance of these methods can suffer when dealing with incomplete modalities, which is a common issue in clinical practice. Existing solutions, such as missing modality synthesis, knowledge distillation, and architecture-based methods, suffer from drawbacks such as long training times, high model complexity, and poor scalability. Method: This paper proposes IMS2Trans, a novel lightweight scalable Swin Transformer network by utilizing a single encoder to extract latent feature maps from all available modalities. This unified feature extraction process enables efficient information sharing and fusion among the modalities, resulting in efficiency without compromising segmentation performance even in the presence of missing modalities. Results: Two datasets, BraTS 2018 and BraTS 2020, containing incomplete modalities for brain tumor segmentation are evaluated against popular benchmarks. On the BraTS 2018 dataset, our model achieved higher average Dice similarity coefficient (DSC) scores for the whole tumor, tumor core, and enhancing tumor regions (86. 57, 75. 67, and 58. 28, respectively), in comparison with a state-of-the-art model, i. e. mmFormer (86. 45, 75. 51, and 57. 79, respectively). Similarly, on the BraTS 2020 dataset, our model scored higher DSC scores in these three brain tumor regions (87. 33, 79. 09, and 62. 11, respectively) compared to mmFormer (86. 17, 78. 34, and 60. 36, respectively). We also conducted a Wilcoxon test on the experimental results, and the generated p -value confirmed that our model’s performance was statistically significant. Moreover, our model exhibits significantly reduced complexity with only 4. 47 M parameters, 121. 89G FLOPs, and a model size of 77. 13 MB, whereas mmFormer comprises 34. 96 M parameters, 265. 79 G FLOPs, and a model size of 559. 74 MB. These indicate our model, being light-weighted with significantly reduced parameters, is still able to achieve better performance than a state-of-the-art model. Conclusion: By leveraging a single encoder for processing the available modalities, IMS2Trans offers notable scalability advantages over methods that rely on multiple encoders. This streamlined approach eliminates the need for maintaining separate encoders for each modality, resulting in a lightweight and scalable network architecture. The source code of IMS2Trans and the associated weights are both publicly available at https: //github. com/hudscomdz/IMS2Trans.

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AIIM Journal 2024 Journal Article

Tackling heterogeneity in medical federated learning via aligning vision transformers

Erfan Darzi
Yiqing Shen
Yangming Ou
Nanna M. Sijtsema
P.M.A van Ooijen

Federated learning enables training models on distributed, privacy-sensitive medical imaging data. However, data heterogeneity across participating institutions leads to reduced model performance and fairness issues, especially for underrepresented datasets. To address these challenges, we propose leveraging the multi-head attention mechanism in Vision Transformers to align the representations of heterogeneous data across clients. By focusing on the attention mechanism as the alignment objective, our approach aims to improve both the accuracy and fairness of federated learning models in medical imaging applications. We evaluate our method on the IQ-OTH/NCCD Lung Cancer dataset, simulating various levels of data heterogeneity using Latent Dirichlet Allocation (LDA). Our results demonstrate that our approach achieves competitive performance compared to state-of-the-art federated learning methods across different heterogeneity levels and improves the performance of models for underrepresented clients, promoting fairness in the federated learning setting. These findings highlight the potential of leveraging the multi-head attention mechanism to address the challenges of data heterogeneity in medical federated learning.

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NeurIPS Conference 2023 Conference Paper

Graph Denoising Diffusion for Inverse Protein Folding

Kai Yi
Bingxin Zhou
Yiqing Shen
Pietro Lió
Yuguang Wang

Inverse protein folding is challenging due to its inherent one-to-many mapping characteristic, where numerous possible amino acid sequences can fold into a single, identical protein backbone. This task involves not only identifying viable sequences but also representing the sheer diversity of potential solutions. However, existing discriminative models, such as transformer-based auto-regressive models, struggle to encapsulate the diverse range of plausible solutions. In contrast, diffusion probabilistic models, as an emerging genre of generative approaches, offer the potential to generate a diverse set of sequence candidates for determined protein backbones. We propose a novel graph denoising diffusion model for inverse protein folding, where a given protein backbone guides the diffusion process on the corresponding amino acid residue types. The model infers the joint distribution of amino acids conditioned on the nodes' physiochemical properties and local environment. Moreover, we utilize amino acid replacement matrices for the diffusion forward process, encoding the biologically-meaningful prior knowledge of amino acids from their spatial and sequential neighbors as well as themselves, which reduces the sampling space of the generative process. Our model achieves state-of-the-art performance over a set of popular baseline methods in sequence recovery and exhibits great potential in generating diverse protein sequences for a determined protein backbone structure.

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