Author name cluster

Hang Fan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

EAAI Journal 2025 Journal Article

A consistency regularization-based approach integrating anatomical structural relationships and organ category representations for multi-organ segmentation in pigs

Xiang Pan
Hang Fan
Jianlan Wang
Yan Fu
Wei Chu
Weipeng Tai
Jing Gu
Jianming Ni

In modern biomedical research and livestock management, accurate multi-organ segmentation in pigs is essential for breeding programs. However, current methods face challenges due to low imaging contrast, size disparities, and organ shape variability. Additionally, the manual annotation of computed tomography (CT) scans is labor-intensive and costly, limiting available labeled samples. To address these issues, we propose a consistency regularization-based network guided by anatomical structural relationships and global organ category representations, specifically designed for multi-organ segmentation using a limited number of annotated CT scan samples from pigs. Specifically, we designed the SpatialLink Gated Recurrent Unit (GRU) module to extract anatomical structural information and capture dynamic spatial relationships between organs, thereby minimizing segmentation biases caused by organ shape variations. Moreover, we developed the Organ Category Coding module and Guidance module, which integrate consistency regularization and attention mechanisms, enabling the network to accurately extract global organ category representations during the decoding phase, even with a small number of labeled samples, significantly improving segmentation consistency across organs of different sizes. Additionally, We are the first to apply the Visual State Space block to multi-organ segmentation in pigs, using it to extract contextual information. Experiments on 60 pigs demonstrate that our method achieves state-of-the-art results, with significant improvements in segmentation accuracy for the gallbladder and bladder, including a 9. 8% and 4. 2% Dice score increase, respectively, and a 12. 4% and 6. 2% boost in Jaccard scores compared to compared with a selection of published methods.

Details DOI

NeurIPS Conference 2025 Conference Paper

Align-DA: Align Score-based Atmospheric Data Assimilation with Multiple Preferences

Jing-An Sun
Hang Fan
Junchao Gong
Ben Fei
Kun Chen
Fenghua Ling
Wenlong Zhang
Wanghan Xu

Data assimilation (DA) aims to estimate the full state of a dynamical system by combining partial and noisy observations with a prior model forecast, commonly referred to as the background. In atmospheric applications, this problem is fundamentally ill-posed due to the sparsity of observations relative to the high-dimensional state space. Traditional methods address this challenge by simplifying background priors to regularize the solution, which are empirical and require continual tuning for application. Inspired by alignment techniques in text-to-image diffusion models, we propose Align-DA, which formulates DA as a generative process and uses reward signals to guide background priors—replacing manual tuning with data-driven alignment. Specifically, we train a score-based model in the latent space to approximate the background-conditioned prior, and align it using three complementary reward signals for DA: (1) assimilation accuracy, (2) forecast skill initialized from the assimilated state, and (3) physical adherence of the analysis fields. Experiments with multiple reward signals demonstrate consistent improvements in analysis quality across different evaluation metrics and observation-guidance strategies. These results show that preference alignment, implemented as a soft constraint, can automatically adapt complex background priors tailored to DA, offering a promising new direction for advancing the field.

PDF Details

NeurIPS Conference 2025 Conference Paper

LoRA-EnVar: Parameter-Efficient Hybrid Ensemble Variational Assimilation for Weather Forecasting

Yi Xiao
Hang Fan
Kun Chen
Ye Cao
Ben Fei
Wei Xue
Lei Bai

Accurate estimation of background error (i. e. , forecast error) distribution is critical for effective data assimilation (DA) in numerical weather prediction (NWP). In state-of-the-art operational DA systems, it is common to account for the temporal evolution of background errors by employing hybrid methods, which blend a static climatological covariance with a flow-dependent ensemble-derived component. While effective to some extent, these methods typically assume Gaussian-distributed errors and rely heavily on hand-crafted covariance structures and domain expertise, limiting their ability to capture the complex, non-Gaussian nature of atmospheric dynamics. In this work, we propose LoRA-EnVar, a novel hybrid ensemble variational DA algorithm that integrates low-rank adaptation (LoRA) into a deep generative modeling framework. We first learn a climatological background error distribution using a variational autoencoder (VAE) trained on historical data. To incorporate flow-dependent uncertainty, we introduce LoRA modules that efficiently adapt the learned distribution in response to flow-dependent ensemble perturbations. Our approach supports online finetuning, enabling dynamic updates of the background error distribution without catastrophic forgetting. We validate LoRA-EnVar in high-resolution assimilation settings using the FengWu forecast model and simulated observations from ERA5 reanalysis. Experimental results show that LoRA-EnVar significantly improves assimilation accuracy over models assuming static background error distribution and achieves comparable or better performance than full finetuning while reducing the number of trainable parameters by three orders of magnitude. This demonstrates the potential of parameter-efficient adaptation for scalable, non-Gaussian DA in operational meteorology.

PDF Details