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Yinhao Li

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8 papers

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JBHI Journal 2026 Journal Article

Multimodal Graph Learning With Multi-Hypergraph Reasoning Networks for Focal Liver Lesion Classification in Multimodal Magnetic Resonance Imaging

Shaocong Mo
Ming Cai
Lanfen Lin
Ruofeng Tong
Fang Wang
Qingqing Chen
Wenbin Ji
Yinhao Li

Multimodal magnetic resonance imaging (MRI) is instrumental in differentiating liver lesions. The major challenge involves modeling reliable connections and simultaneously learning complementary information across various MRI sequences. While previous studies have primarily focused on multimodal integration in a pair-wise manner using few modalities, our research seeks to advance a more comprehensive understanding of interaction modeling by establishing complex high-order correlations among the diverse modalities in multimodal MRI. In this paper, we introduce a multimodal graph learning with multi-hypergraph reasoning network to capture the full spectrum of both pair-wise and group-wise relationships among different modalities. Specifically, a weight-shared encoder extracts features from regions of interest (ROI) images across all modalities. Subsequently, a collection of uniform hypergraphs are constructed with varying vertex configurations, allowing for the modeling of not only pair-wise correlations but also the high-order collaborations for relational reasoning. Following information propagation through the hypergraph message passing, adaptive intra-modality fusion module is proposed to effectively fuse feature representations from different hypergraphs of the same modality. Finally, all refined features are concatenated to prepare for the classification task. Our experimental evaluations, including focal liver lesions classification using the LLD-MMRI2023 dataset and early recurrence prediction of hepatocellular carcinoma using our internal datasets, demonstrate that our method significantly surpasses the performance of existing approaches, indicating the effectiveness of our model in handling both pair-wise and group-wise interactions across multiple modalities.

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JBHI Journal 2026 Journal Article

Rethinking Feature Interactions for Medical Image Segmentation: A Unified Hierarchical Aggregation Framework with Boundary Guidance

Chunlin Yu
Yinhao Li
Jiaxun Li
Zheng Zhao
Taohong Zhang

Medical image segmentation is a crucial task of medical image analysis and computer vision. Medical images, compared to natural ones, contain more complex semantic information, making feature learning more challenging. Existing encoder-decoder architectures are limited by inadequate cross-scale interaction and insufficient boundary modeling in their feature fusion designs. To address this, we propose a Hierarchical Feature Interaction network with Boundary guidance (HFIBNet), which unifies dynamic cross-level feature fusion and explicit edge supervision within a coarse-to-fine segmentation framework. Specifically, we introduce a Boundary Prediction (BP) module to extract boundary-aware features that guide the fusion process. A Cross-Level Feature Fusion (CLFF) module is designed to promote semantic interaction across adjacent encoder stages, while the Edge Feature Aggregation (EFA) module propagates boundary cues hierarchically to enhance structural consistency. Furthermore, a Partially Parallel Decoder (PPD) generates a coarse global prediction, which is progressively refined by a Global-Local Feature Enrichment (GLFE) module, mimicking the clinical annotation workflow from coarse localization to fine delineation. Extensive experiments on ten public medical segmentation datasets across four distinct tasks demonstrate that HFIBNet consistently outperforms existing state-of-the-art methods. The code is available available at https://github.com/ukeLin/HFIBNet.

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JBHI Journal 2026 Journal Article

SPA: Leveraging the SAM With Spatial Priors Adapter for Enhanced Medical Image Segmentation

Jihong Hu
Yinhao Li
Rahul Kumar Jain
Lanfen Lin
Yen-Wei Chen

The Segment Anything Model (SAM) has gained renown for its success in image segmentation, benefiting significantly from its pretraining on extensive datasets and its interactive prompt-based segmentation approach. Although highly effective in natural (real-world) image segmentation tasks, the SAM model encounters significant challenges in medical imaging due to the inherent differences between these two domains. To address these challenges, we propose the Spatial Prior Adapter (SPA) scheme, a parameter-efficient fine-tuning strategy that enhances SAM's adaptability to medical imaging tasks. SPA introduces two novel modules: the Spatial Prior Module (SPM), which captures localized spatial features through convolutional layers, and the Feature Communication Module (FCM), which integrates these features into SAM's image encoder via cross-attention mechanisms. Furthermore, we develop a Multiscale Feature Fusion Module (MSFFM) to enhance SAM's end-to-end segmentation capabilities by effectively aggregating multiscale contextual information. These lightweight modules require minimal computational resources while significantly boosting segmentation performance. Our approach demonstrates superior performance in both prompt-based and end-to-end segmentation scenarios through extensive experiments on publicly available medical imaging datasets. Performance highlights the potential of the proposed method to bridge the gap between foundation models and domain-specific medical imaging tasks. This advancement paves the way for more effective AI-assisted medical diagnostic systems.

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JBHI Journal 2026 Journal Article

Topology-aware Diffusion Schrödinger Bridge for Unpaired H&E-to-IHC Stain Translation

Chujie Zhang
Yangyang Xie
Jihong Hu
Xiaoyu Shi
Yinhao Li
Xiao Liang
Lanfen Lin
Yen-Wei Chen

Unpaired H&E-to-IHC Stain Translation aims to generate immunohistochemistry (IHC) staining from Hematoxylin and Eosin (H&E) staining. It offers clearer diagnostic insights and potentially expands access to advanced pathology services in resource-limited areas. This task faces two primary challenges: capturing target domain style characteristics and preserving topological features in histological images. Recently, Schrödinger Bridge (SB)-based methods have offered a solution for unpaired image-to-image translation, addressing the mode collapse and artifact issues in CycleGAN-based approaches, as well as the Gaussian prior assumption limitation in diffusion-based methods. While SB-based methods suffer from the curse of dimensionality with high-resolution images, the Unpaired Neural Schrödinger Bridge (UNSB) overcomes this challenge and achieves state-of-the-art (SOTA) performance on natural images. However, UNSB has two key issues in histological images: (1) loss of topological features and (2) IHC staining representation. UNSB focuses only on the optimal path from source to target domains, ignoring local structure paths. Convolutional neural networks (CNNs) do not perfectly preserve critical anatomical structures due to limitations like receptive field size or model capacity. To address these challenges, we introduce the T opology-aware D iffusion S chrödinger B ridge ( TDSB ), integrating a Topology Guidance (TG) module and Dual-Domain Adaptive Patch-based noise contrastive estimation (DDAP). Experiments on seven translation tasks across three datasets show that our method achieves SOTA performance in unpaired H&E-to-IHC stain translation. Clinical evaluation through pathologists' assessments further validates the effectiveness of our method.

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JBHI Journal 2025 Journal Article

SAMA: A Self-and-Mutual Attention Network for Accurate Recurrence Prediction of Non-Small Cell Lung Cancer Using Genetic and CT Data

Yang Ai
Jing Liu
Yinhao Li
Fang Wang
Xiuju Du
Rahul Kumar Jain
Lanfen Lin
Yen-Wei Chen

Accurate preoperative recurrence prediction for non-small cell lung cancer (NSCLC) is a challenging issue in the medical field. Existing studies primarily conduct image and molecular analyses independently or directly fuse multimodal information through radiomics and genomics, which fail to fully exploit and effectively utilize the highly heterogeneous cross-modal information at different levels and model the complex relationships between modalities, resulting in poor fusion performance and becoming the bottleneck of precise recurrence prediction. To address these limitations, we propose a novel unified framework, the Self-and-Mutual Attention (SAMA) Network, designed to efficiently fuse and utilize macroscopic CT images and microscopic gene data for precise NSCLC recurrence prediction, integrating handcrafted features, deep features, and gene features. Specifically, we design a Self-and-Mutual Attention Module that performs three-stage fusion: the self-enhancement stage enhances modality-specific features; the gene-guided and CT-guided cross-modality fusion stages perform bidirectional cross-guidance on the self-enhanced features, complementing and refining each modality, enhancing heterogeneous feature expression; and the optimized feature aggregation stage ensures the refined interactive features for precise prediction. Extensive experiments on both publicly available datasets from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) demonstrate that our method achieves state-of-the-art performance and exhibits broad applicability to various cancers.

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JBHI Journal 2024 Journal Article

Segmentation Guided Crossing Dual Decoding Generative Adversarial Network for Synthesizing Contrast-Enhanced Computed Tomography Images

Yulin Yang
Qingqing Chen
Yinhao Li
Fang Wang
Xian-Hua Han
Yutaro Iwamoto
Jing Liu
Lanfen Lin

Although contrast-enhanced computed tomography (CE-CT) images significantly improve the accuracy of diagnosing focal liver lesions (FLLs), the administration of contrast agents imposes a considerable physical burden on patients. The utilization of generative models to synthesize CE-CT images from non-contrasted CT images offers a promising solution. However, existing image synthesis models tend to overlook the importance of critical regions, inevitably reducing their effectiveness in downstream tasks. To overcome this challenge, we propose an innovative CE-CT image synthesis model called Segmentation Guided Crossing Dual Decoding Generative Adversarial Network (SGCDD-GAN). Specifically, the SGCDD-GAN involves a crossing dual decoding generator including an attention decoder and an improved transformation decoder. The attention decoder is designed to highlight some critical regions within the abdominal cavity, while the improved transformation decoder is responsible for synthesizing CE-CT images. These two decoders are interconnected using a crossing technique to enhance each other's capabilities. Furthermore, we employ a multi-task learning strategy to guide the generator to focus more on the lesion area. To evaluate the performance of proposed SGCDD-GAN, we test it on an in-house CE-CT dataset. In both CE-CT image synthesis tasks–namely, synthesizing ART images and synthesizing PV images–the proposed SGCDD-GAN demonstrates superior performance metrics across the entire image and liver region, including SSIM, PSNR, MSE, and PCC scores. Furthermore, CE-CT images synthetized from our SGCDD-GAN achieve remarkable accuracy rates of 82. 68%, 94. 11%, and 94. 11% in a deep learning-based FLLs classification task, along with a pilot assessment conducted by two radiologists.

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AAAI Conference 2023 Conference Paper

BEVDepth: Acquisition of Reliable Depth for Multi-View 3D Object Detection

Yinhao Li
Zheng Ge
Guanyi Yu
Jinrong Yang
Zengran Wang
Yukang Shi
Jianjian Sun
Zeming Li

In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View~(BEV) 3D object detection. Our work is based on a key observation -- depth estimation in recent approaches is surprisingly inadequate given the fact that depth is essential to camera 3D detection. Our BEVDepth resolves this by leveraging explicit depth supervision. A camera-awareness depth estimation module is also introduced to facilitate the depth predicting capability. Besides, we design a novel Depth Refinement Module to counter the side effects carried by imprecise feature unprojection. Aided by customized Efficient Voxel Pooling and multi-frame mechanism, BEVDepth achieves the new state-of-the-art 60.9% NDS on the challenging nuScenes test set while maintaining high efficiency. For the first time, the NDS score of a camera model reaches 60%. Codes have been released.

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AAAI Conference 2023 Conference Paper

BEVStereo: Enhancing Depth Estimation in Multi-View 3D Object Detection with Temporal Stereo

Yinhao Li
Han Bao
Zheng Ge
Jinrong Yang
Jianjian Sun
Zeming Li

Restricted by the ability of depth perception, all Multi-view 3D object detection methods fall into the bottleneck of depth accuracy. By constructing temporal stereo, depth estimation is quite reliable in indoor scenarios. However, there are two difficulties in directly integrating temporal stereo into outdoor multi-view 3D object detectors: 1) The construction of temporal stereos for all views results in high computing costs. 2) Unable to adapt to challenging outdoor scenarios. In this study, we propose an effective method for creating temporal stereo by dynamically determining the center and range of the temporal stereo. The most confident center is found using the EM algorithm. Numerous experiments on nuScenes have shown the BEVStereo's ability to deal with complex outdoor scenarios that other stereo-based methods are unable to handle. For the first time, a stereo-based approach shows superiority in scenarios like a static ego vehicle and moving objects. BEVStereo achieves the new state-of-the-art in the camera-only track of nuScenes dataset while maintaining memory efficiency. Codes have been released.

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