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Lanlan Wang

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3 papers

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JBHI Journal 2026 Journal Article

Customized SAM-Med3D With Multi-View Representation Fusion and Age-Grade Stratified Loss for Glioma Survival Risk Prediction

Xinyu Li
Hulin Kuang
Jin Liu
Lanlan Wang
Pengcheng Shu
Mengshen He
Jianxin Wang

Survival risk prediction is crucial for personalized treatment of gliomas. Medical image foundational models can explore complex medical features, which are critical for prognosis in gliomas. We propose SAM-Risk, which uses a customized SAM-Med3D with multi-view representation fusion and clinical knowledge-based age-grade stratified loss for glioma survival risk prediction. First, to utilize potential interactions between multiple views at an early stage, we design a 3D representation generation module that transforms 1D handcrafted radiomics and clinical features into 3D representations, which are fused with multimodal MRIs through a multi-view representation fusion module. The fused representation is fed into the customized SAM-Med3D, fine-tuned using LoRA and a disparity function to extract survival risk-related features. We design a feature refinement module to explore the inter-channel relationships among the outputs of the fine-tuned SAM-Med3D. Additionally, we propose an age-grade stratified loss based on glioma prognosis standards to make the predicted risk more consistent with clinical prior knowledge. Validated on two publicly available UCSF-PDGM and BraTS2020 datasets, SAM-Risk achieves a C-index of 75. 08% and 73. 67%, respectively, outperforming several survival risk prediction methods.

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JBHI Journal 2026 Journal Article

Generalizable Seizure Prediction With LLMs: Converting EEG to Textual Representations

Yuchang Zhao
Aiping Liu
Chang Li
Lanlan Wang
Ruobing Qian
Xun Chen

Seizure prediction through scalp electroencephalogram (EEG) holds considerable practical potential. The primary challenge faced by existing algorithms lies in the individual heterogeneity, which hinders the generalizability of models to new patients. Additionally, inconsistencies in channel settings across various epilepsy centers further limit the applicability of models to diverse datasets. To address these challenges, we incorporate large language models (LLMs) into EEG analysis and propose a novel seizure prediction method based on LLMs (SPLLM), significantly enhancing both model generalizability and applicability. Specifically, this approach reprograms LLMs by transforming EEG signals into textual representations compatible with LLMs via a single-channel pre-training strategy. The method integrates cross-domain knowledge from both text and EEG data through a cross-attention mechanism, utilizing autoregressive pretrained LLMs to capture the temporal dependencies inherent in EEG signals. Moreover, the cross-domain generalization ability of LLMs alleviates patient heterogeneity, while the single-channel pre-training strategy enables the model to adapt to diverse channel settings. On two public datasets and one private dataset, SPLLM increases the average AUC by 8. 2%, and the average balanced accuracy by 8. 4% compared to existing methods. Experimental results demonstrate that the proposed method not only enhances cross-patient prediction accuracy but also adapts to data from different datasets, offering a scalable solution for the clinical application of seizure prediction.

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JBHI Journal 2024 Journal Article

Generating Biomedical Hypothesis With Spatiotemporal Transformers

Huiwei Zhou
Lanlan Wang
Weihong Yao
Wenchu Li
Hao Zhou
Hongyun Zeng

Generating biomedical hypotheses is a difficult task as it requires uncovering the implicit associations between massive scientific terms from a large body of published literature. A recent line of Hypothesis Generation (HG) approaches - temporal graph-based approaches - have shown great success in modeling temporal evolution of term-pair relationships. However, these approaches model the temporal evolution of each term or term-pair with Recurrent Neural Network (RNN) independently, which neglects the rich covariation among all terms or term-pairs while ignoring direct dependencies between any two timesteps in a temporal sequence. To address this problem, we propose a Spatiotemporal Transformer-based Hypothesis Generation (STHG) method to interleave spatial covariation and temporal progression in a unified framework for constructing direct connections between any two term-pairs while modeling the temporal relevance between any two timesteps. Experiments on three biomedical relationship datasets show that STHG outperforms the state-of-the-art methods.

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