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Junjie He

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5 papers

2 author rows

YNIMG Journal 2026 Journal Article

Multimodal radiomics of precisely segmented hippocampal subfields: Iron deposition and structural biomarkers for early diagnosis of Alzheimer's disease

Dongxue Li
Junjie He
Benqin Liu
Lin Zhu
Yuezong Yang
Yunsong Peng
Lisha Nie
Rongpin Wang

Profiling imaging biomarkers of prodromal Alzheimer's disease (AD) against AD dementia may aid earlier diagnosis, yet approaches jointly capturing iron-related pathology and hippocampal subfield heterogeneity remain scarce. We developed a hippocampal-subfield multimodal radiomics framework integrating quantitative susceptibility mapping (QSM) and 3D T1-weighted MRI. A primary cohort of 92 participants (50 prodromal AD, 42 CE dementia) and an independent external cohort of 30 (15/15) were included. Twenty-four hippocampal subfields were segmented on super-resolution T1 images and propagated to co-registered QSM for feature extraction. Radiomic features were condensed into a radiomics score (Rad-score) via a training-only selection pipeline. Using the Rad-score as the sole predictor, a support vector machine (SVM) classifier was trained. On the external cohort, the SVM achieved an area under the receiver operating characteristic curve of 0.85 and an accuracy of 0.83. The predictive signature was dominated by QSM texture features in Cornu Ammonis 1 and the granule cell layer of the dentate gyrus, complemented by T1 first-order heterogeneity. Modality ablation suggested potential-but not definitive-complementarity of multimodal integration. This framework shows promise for AD stage classification and warrants further validation in larger independent cohorts.

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YNIMG Journal 2026 Journal Article

PI-uMSS: Prior information-based unsupervised magnetic source separation in quantitative susceptibility mapping

Junjie He
Bangkang Fu
Cen Pan
Lisha Nie
Rui Xu
Zi Xu
Rongpin Wang

Magnetic source separation (MSS) in quantitative susceptibility mapping (QSM) provides a powerful tool to disentangle paramagnetic and diamagnetic contributions, enabling more accurate quantification of brain iron and myelin alterations. However, existing MSS approaches typically depend on approximations derived from reversible transverse relaxation (R2'=R2∗-R2) or extrapolate from a limited number of brain regions to perform whole-brain separation. Furthermore, current deep learning-based methods often require extensive and high-quality labels, which are difficult to obtain. To address these limitations, we propose an unsupervised MSS framework guided by prior information and constrained by physics-informed loss functions to improve separation fidelity. The proposed model directly processes whole-brain QSM and R2∗ data, infers intermediate parameters, and reconstructs the spatial distributions of paramagnetic and diamagnetic sources via biophysical modeling. Experimental results show that the method achieves high structural similarity (SSIM = 0.9945 for paramagnetic and 0.9942 for diamagnetic components) and a low normalized mean square error (0.11) relative to the original QSM, demonstrating robust and consistent source decomposition performance. Code is available at https://github.com/TyrionJ/PI-uMSS.

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ICLR Conference 2025 Conference Paper

Knowledge Graph Finetuning Enhances Knowledge Manipulation in Large Language Models

Hanzhu Chen
Xu Shen 0001
Jie Wang 0005
Zehao Wang
Qitan Lv
Junjie He
Rong Wu
Feng Wu 0001

Despite the impressive performance of general large language models(LLMs), many of their applications in specific domains (e.g., low-data and knowledge-intensive) still confront significant challenges. Supervised fine-tuning (SFT)---where a general LLM is further trained on a small labeled dataset to adapt for specific tasks or domains---has shown great power for developing domain-specific LLMs. However, existing SFT data primarily consist of Question and Answer (Q&A) pairs, which poses a significant challenge for LLMs to comprehend the correlation and logic of knowledge underlying the Q&A. To address this challenge, we propose a conceptually flexible and general framework to boost SFT, namely Knowledge Graph-Driven Supervised Fine-Tuning (KG-SFT). The key idea of KG-SFT is to generate high-quality explanations for each Q&A pair via a structured knowledge graph to enhance the knowledge comprehension and manipulation of LLMs. Specifically, KG-SFT consists of three components: Extractor, Generator, and Detector. For a given Q&A pair, (i) Extractor first identifies entities within Q&A pairs and extracts relevant reasoning subgraphs from external KGs, (ii) Generator then produces corresponding fluent explanations utilizing these reasoning subgraphs, and (iii) finally, Detector performs sentence-level knowledge conflicts detection on these explanations to guarantee the reliability. KG-SFT focuses on generating high-quality explanations to improve the quality of Q&A pair, which reveals a promising direction for supplementing existing data augmentation methods. Extensive experiments on fifteen different domains and six different languages demonstrate the effectiveness of KG-SFT, leading to an accuracy improvement of up to 18% and an average of 8.7% in low-data scenarios.

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NeurIPS Conference 2024 Conference Paper

Mixtures of Experts for Audio-Visual Learning

Ying Cheng
Yang Li
Junjie He
Rui Feng

With the rapid development of multimedia technology, audio-visual learning has emerged as a promising research topic within the field of multimodal analysis. In this paper, we explore parameter-efficient transfer learning for audio-visual learning and propose the Audio-Visual Mixture of Experts (\ourmethodname) to inject adapters into pre-trained models flexibly. Specifically, we introduce unimodal and cross-modal adapters as multiple experts to specialize in intra-modal and inter-modal information, respectively, and employ a lightweight router to dynamically allocate the weights of each expert according to the specific demands of each task. Extensive experiments demonstrate that our proposed approach \ourmethodname achieves superior performance across multiple audio-visual tasks, including AVE, AVVP, AVS, and AVQA. Furthermore, visual-only experimental results also indicate that our approach can tackle challenging scenes where modality information is missing. The source code is available at \url{https: //github. com/yingchengy/AVMOE}.

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YNIMG Journal 2023 Journal Article

msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping

Junjie He
Yunsong Peng
Bangkang Fu
Yuemin Zhu
Lihui Wang
Rongpin Wang

Quantitative susceptibility mapping (QSM) has been applied to the measurement of iron deposition and the auxiliary diagnosis of neurodegenerative disease. There still exists a dipole inversion problem in QSM reconstruction. Recently, deep learning approaches have been proposed to resolve this problem. However, most of these approaches are supervised methods that need pairs of the input phase and ground-truth. It remains a challenge to train a model for all resolutions without using the ground-truth and only using one resolution data. To address this, we proposed a self-supervised QSM deep learning method based on morphology. It consists of a morphological QSM builder to decouple the dependency of the QSM on acquisition resolution, and a morphological loss to reduce artifacts effectively and save training time efficiently. The proposed method can reconstruct arbitrary resolution QSM on both human data and animal data, regardless of whether the resolution is higher or lower than that of the training set. Our method outperforms the previous best unsupervised method with a 3.6% higher peak signal-to-noise ratio, 16.2% lower normalized root mean square error, and 22.1% lower high-frequency error norm. The morphological loss reduces training time by 22.1% with respect to the cycle gradient loss used in the previous unsupervised methods. Experimental results show that the proposed method accurately measures QSM with arbitrary resolutions, and achieves state-of-the-art results among unsupervised deep learning methods. Research on applications in neurodegenerative diseases found that our method is robust enough to measure significant increase in striatal magnetic susceptibility in patients during Alzheimer's disease progression, as well as significant increase in substantia nigra susceptibility in Parkinson's disease patients, and can be used as an auxiliary differential diagnosis tool for Alzheimer's disease and Parkinson's disease.

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