arXiv subjects

Yulong Dou

Publications and source records attributed to Yulong Dou.

3 recordsLinked to original sources

arXiv2026

Taming foundation model with invariance-oriented pre-training for broad-spectrum EEG analysis across signal-level, brain-state, and brain-health tasks

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.

cs.LG↗

arXiv2024

TeethDreamer: 3D Teeth Reconstruction from Five Intra-oral Photographs

Orthodontic treatment usually requires regular face-to-face examinations to monitor dental conditions of the patients. When in-person diagnosis is not feasible, an alternative is to utilize five intra-oral photographs for remote dental monitoring. However, it lacks of 3D information, and how to reconstruct 3D dental models from such sparse view photographs is a challenging problem. In this study, we propose a 3D teeth reconstruction framework, named TeethDreamer, aiming to restore the shape and position of the upper and lower teeth. Given five intra-oral photographs, our approach first leverages a large diffusion model's prior knowledge to generate novel multi-view images with known poses to address sparse inputs and then reconstructs high-quality 3D teeth models by neural surface reconstruction. To ensure the 3D consistency across generated views, we integrate a 3D-aware feature attention mechanism in the reverse diffusion process. Moreover, a geometry-aware normal loss is incorporated into the teeth reconstruction process to enhance geometry accuracy. Extensive experiments demonstrate the superiority of our method over current state-of-the-arts, giving the potential to monitor orthodontic treatment remotely. Our code is available at https://github.com/ShanghaiTech-IMPACT/TeethDreamer

cs.CV↗

arXiv2023

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

Orthodontics focuses on rectifying misaligned teeth (i.e., malocclusions), affecting both masticatory function and aesthetics. However, orthodontic treatment often involves complex, lengthy procedures. 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.

cs.CV↗