Taming Foundation Model with Invariance-Oriented Pre-Training for Broad-Spectrum EEG Analysis across Signal-Level, Brain-State, and Brain-Health Tasks

Yulong Dou, Han Wu, Guo Chen, Fangmao Ju, Zhiming Cui, Dinggang Shen

Under Submission 2026

Electroencephalography (EEG) is a widely used window into human brain function, but most EEG models remain tied to a one-dataset-one-model supervised paradigm. Here we present INCEPT, an invariance-oriented EEG foundation model trained on over 11,000 hours of unlabelled clinical EEG. (view more ) Electroencephalography (EEG) is a widely used window into human brain function, but most EEG models remain tied to a one-dataset-one-model supervised paradigm. Recent EEG foundation models offer a route toward reusable representations, but most remain reconstruction-centered, assuming that EEG content predictable from local context is necessarily transferable neural information. Here we present INCEPT, an invariance-oriented EEG foundation model trained on over 11,000 hours of unlabelled clinical EEG. Rather than prioritizing signal recovery alone, INCEPT learns representation-level stability across correlated EEG observations, separating stable neural structure and essential subject-sensitive information from the nuisance variability that dominates scalp recordings while preserving subject-, state- and condition-discriminative information. We evaluate INCEPT on a broad-spectrum benchmark of ten datasets spanning three levels of post-acquisition EEG analysis: signal-level assessment, brain-state decoding, and brain-health evaluation. INCEPT ranks first among recent EEG foundation models on 26 of 30 linear-probing metrics and 24 of 30 fine-tuning metrics, and also surpasses strong task-specific specialist encoders across diverse downstream settings. Objective ablations and representation analyses further show that invariance-oriented pre-training improves transfer and organizes subject-sensitive neural representations beyond reconstruction alone. These results establish invariance learning as a promising principle for building reusable EEG foundation models. (view less )

[Paper] [Code]

COMET: Controllable Orthodontic Video Editing with Multi-view Estimation

Lanzhuju Mei, Yulong Dou, Han Wu, Guo Chen, Zhiming Cui, Dinggang Shen

Under Submission 2026

Orthodontic treatment visualization is critical for patient decision-making and expectation management during lengthy and complex dental alignment procedures. We introduce COMET, a diffusion-based framework for controllable orthodontic video editing with multi-view estimation, which predicts structurally consistent post-treatment outcomes from pre-treatment videos and provides intuitive dynamic visualizations of expected results. (view more ) Orthodontic treatment visualization is critical for patient decision-making and expectation management during lengthy and complex dental alignment procedures. Existing methods predict static 2D images, and patients struggle to envision dynamic treatment outcomes from fixed-pose photographs alone. Two difficulties stand in the way of video-based prediction. First, paired pre- and post-treatment photographs are rarely available in real-world settings, so there is little direct supervision for learning orthodontic transformations. Second, the fine geometry of the teeth must stay consistent while head pose, viewpoint, and tooth visibility change from frame to frame. To address these difficulties, we introduce COMET, a diffusion-based framework for Controllable Orthodontic video editing with Multi-view EsTimation, which predicts structurally consistent post-treatment outcomes from pre-treatment videos and provides intuitive dynamic visualizations of expected results. COMET integrates an adapted ControlNet into a pretrained video diffusion model and augments it with two conditioning modules: (1) an Orthodontic Predictor that learns treatment-induced tooth movements from paired clinical 3D dental models and transforms the pre-treatment image in the first frame into a post-treatment image, and (2) a Multi-view Generator that estimates tooth contours across viewpoints so that the dental structures stay consistent across frames. In a user study with 18 participants, including three dentists, COMET receives the highest ratings on all criteria, and it improves tooth integrity from 0.74 to 0.87 over the best competing method on real-world videos. COMET provides a practical tool for offline orthodontic visualization and patient consultation. (view less )

A Unified Latent Diffusion for High-Fidelity Any-to-Any Brain Modality Synthesis

Yulong Dou*, Guo Chen*, Chenfan Xu, Yulin Wang, Zhe Xu, Zhiming Cui, Dinggang Shen (* equal contribution)

6th Deep Generative Models Workshop at MICCAI (DGM4MICCAI) 2026 (Long Oral)

Comprehensive neurological diagnosis relies on the synergy of multimodal data from MRI, CT, and PET to capture distinct anatomical and pathological markers. To address incomplete imaging suites and the limitations of paired training data, we present BrainDiff, a unified latent diffusion framework for flexible any-to-any brain modality synthesis. (view more ) Comprehensive neurological diagnosis relies on the synergy of multimodal data from MRI, CT, and PET to capture distinct anatomical and pathological markers. However, clinical reality often results in incomplete imaging suites due to time constraint, high costs, or patient contraindication. Although cross-modality synthesis offers a potential solution, existing methods are limited by their reliance on strictly paired training data. This constraint prevents them from utilizing abundant single-modality data and from learning the distinct characteristics of each imaging domain, resulting in limited generative fidelity. To address these challenges, we present BrainDiff, a unified latent diffusion framework for flexible any-to-any brain modality synthesis. Our method employs a universal VQ-VAE to project all modalities into a shared latent space, where a prompt-guided diffusion model synthesizes corresponding images conditioned on descriptive prompts (e.g., patient attributes, imaging parameters). During cross-modality synthesis, a ControlNet module is incorporated to provide guidance that preserves anatomical structure of the source-modality image. Extensive multi-institutional experiments demonstrate that BrainDiff achieves state-of-the-art performance, delivering high-fidelity and anatomically consistent results for any-to-any generation. Code is available at https://github.com/douyl/BrainDiff. (view less )

[Code]

OrthoFlow: Decoupled Landmark Prediction and Image Synthesis for Orthodontic Treatment Outcome Visualization

Leyuan Wang*, Yulong Dou*, Jiancheng Yang, Guangying Song, Zhiming Cui (* equal contribution)

International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2026

Predicting post-treatment facial profiles from lateral cephalograms is clinically valuable for orthodontic planning and patient communication, yet remains challenging due to the complex interplay between skeletal, dental, and soft-tissue changes. We propose OrthoFlow, a two-stage decoupled generative framework that separates geometric prediction from image synthesis. (view more ) Predicting post-treatment facial profiles from lateral cephalograms is clinically valuable for orthodontic planning and patient communication, yet remains challenging due to the complex interplay between skeletal, dental, and soft-tissue changes. Conventional approaches either predict discrete clinical indices without visual output, or apply direct image-to-image translation that fails to preserve anatomical consistency. We propose OrthoFlow, a two-stage decoupled generative framework that separates geometric prediction from image synthesis. In the first stage, a Flow Matching model with a Graph-Transformer hybrid backbone predicts post-treatment landmark displacements conditioned on textual treatment plans. In the second stage, a conditional Flow Matching model synthesizes high-fidelity lateral cephalograms guided by the predicted anatomical contours. Experiments on a large-scale orthodontic dataset demonstrate that OrthoFlow achieves superior geometric accuracy and visual fidelity compared to existing methods, validated by both quantitative metrics and clinical evaluation by orthodontists. Our code and dataset are available at https://github.com/ShanghaiTech-IMPACT/OrthoFlow. (view less )

[Code]

BrainParc: Unified Lifespan Brain Parcellation from Structural MR Images

Jiameng Liu, Feihong Liu, Kaicong Sun, Zhiming Cui, Tianyang Sun, Zehong Cao, Jiawei Huang, Shuwei Bai, Yulin Wang, Yulong Dou, Kaicheng Zhang, Caiwen Jiang, Yuyan Ge, Han Zhang, Feng Shi, Dinggang Shen

Nature Computational Science 2026

Accurate brain parcellation from structural MRI (sMRI) across the human lifespan is essential for advancing neuroimaging and neuroscience studies. However, existing methods often struggle to generalize due to intensity and contrast variations across brain maturation, aging, and differences in MRI acquisition protocols, limiting their clinical and research utility. (view more ) To address this, we present BrainParc, a unified parcellation framework that leverages anatomical information invariant to intensity and contrast, enabling accurate, robust, and longitudinally consistent parcellation across heterogeneous dataset without the need for fine-tuning. We curated the largest known sMRI dataset to date, comprising over 93300 T1w MRI data from 19 globally distributed cohorts spanning ages 0-100 years, to train and rigorously evaluate BrainParc. Extensive experiments on both internal and external datasets demonstrate that BrainParc significantly outperforms state-of-the-art methods in delineating 106 brain regions, achieving average Dice scores of 95.48% internally and 94.10% externally. BrainParc consistently shows superior performance across diverse populations and imaging conditions, both quantitatively and qualitatively. Beyond anatomical segmentation, we show that BrainParc enables reliable tracking of brain development and facilitates early diagnosis of neurological disorders, underscoring its potential as a robust and generalizable tool for large-scale neuroimaging studies and clinical translation. (view less )

[Paper] [Code]

CLIK-Diffusion: Clinical Knowledge-informed Diffusion Model for Tooth Alignment

Yulong Dou, Han Wu, Changjian Li, Chen Wang, Tong Yang, Min Zhu, Dinggang Shen, Zhiming Cui

Medical Image Analysis (MedIA) 2025

Traditional semi-automatic methods for tooth alignment involve laborious manual procedures and heavily depend on the expertise of dentists, which often leads to inefficient and prolonged treatment durations. (view more ) Although many automatic methods have been proposed to assist especially the less experienced dentists, they often lack incorporating clinical insight and oversimplify the problem by estimating rigid transformation matrix for each tooth directly from dental point clouds. This over-simplification fails to capture nuanced requirements of orthodontic treatment, i.e., specific clinical rules for effective alignment of misaligned teeth. To address this, we propose CLIK-Diffusion, a clinical knowledge-informed diffusion model for automatic tooth alignment. CLIK-Diffusion formulates the complex problem of tooth alignment as a more manageable landmark transformation problem, which is further refined into a landmark coordinate generation task. Specifically, we first detect landmarks for each tooth by category, and then build our CLIK-Diffusion to learn distribution of normal occlusion. To further encourage the integration of essential clinical knowledge, we design hierarchical constraints from three perspectives: 1) dental-arch level: to constrain arrangement of teeth from a global level; 2) inter-tooth level: to ensure tight contact and avoid unnecessary collision between neighboring teeth; and 3) individual-tooth level: to guarantee correct orientation of each tooth. In this way, our designed CLIK-Diffusion is able to predict the post-orthodontic landmarks that align with clinical knowledge, and then estimate rigid transformation for each tooth based on coordinates of its pre- and post-orthodontic landmarks. We have evaluated our CLIK-Diffusion on various malocclusion cases collected in real-world clinics, and demonstrate its exceptional performance and strong applicability in orthodontic treatment, compared with other state-of-the-art methods. The dataset and code will be released after acceptance of paper. (view less )

[Paper] [Code] [Dataset]

3D Structure-guided Network for Tooth Alignment in 2D Photograph

Yulong Dou, Lanzhuju Mei, Dinggang Shen, Zhiming Cui

The 34th British Machine Vision Conference (BMVC) 2023

Orthodontics focuses on rectifying misaligned teeth (i.e., malocclusions), affecting both masticatory function and aesthetics. However, orthodontic treatment often involves complex, lengthy procedures. (view more ) As such, generating a 2D photograph depicting aligned teeth prior to orthodontic treatment is crucial for effective dentist-patient communication and, more importantly, for encouraging patients to accept orthodontic intervention. In this paper, we propose a 3D structure-guided tooth alignment network that takes 2D photographs as input (e.g., photos captured by smartphones) and aligns the teeth within the 2D image space to generate an orthodontic comparison photograph featuring aesthetically pleasing, aligned teeth. Notably, while the process operates within a 2D image space, our method employs 3D intra-oral scanning models collected in clinics to learn about orthodontic treatment, i.e., projecting the pre- and post-orthodontic 3D tooth structures onto 2D tooth contours, followed by a diffusion model to learn the mapping relationship. Ultimately, the aligned tooth contours are leveraged to guide the generation of a 2D photograph with aesthetically pleasing, aligned teeth and realistic textures. We evaluate our network on various facial photographs, demonstrating its exceptional performance and strong applicability within the orthodontic industry. (view less )

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