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Yining Jiao

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3 papers

2 author rows

TMLR Journal 2026 Journal Article

$\texttt{LucidAtlas}$: Learning Uncertainty-Aware, Covariate-Disentangled, Individualized Atlas Representations

Yining Jiao
Sreekalyani Bhamidi
Carlton Jude ZDANSKI
Huaizhi Qu
Julia S Kimbell
Andrew Prince
Cameron P Worden
Samuel Kirse

Interpreting how covariates influence spatially structured biological variation — for example, how pediatric airway geometry changes along the airway and across a growing population — remains a key challenge in developing models suitable for clinical application. We present $\texttt{LucidAtlas}$, a versatile framework for modeling and interpreting spatially varying information with associated covariates. To address the limitations of neural additive models when analyzing dependent covariates, we introduce a marginalization approach that enables accurate explanations of how combinations of covariates shape the learned atlas. $\texttt{LucidAtlas}$ integrates covariate interpretation, spatial representation, individualized prediction, population distribution analysis, and out-of-distribution detection into a single interpretable model. We validate its effectiveness on a synthetic spatiotemporal dataset, the OASIS brain volume dataset, and a pediatric airway shape dataset. Our findings underscore the critical role of by-construction interpretable models in advancing scientific discovery. The implementation is publicly available at https://github.com/****.

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ICLR Conference 2024 Conference Paper

NAISR: A 3D Neural Additive Model for Interpretable Shape Representation

Yining Jiao
Carlton J. Zdanski
Julia S. Kimbell
Andrew Prince
Cameron Worden
Samuel Kirse
Christopher Rutter
Benjamin Shields

Deep implicit functions (DIFs) have emerged as a powerful paradigm for many computer vision tasks such as 3D shape reconstruction, generation, registration, completion, editing, and understanding. However, given a set of 3D shapes with associated covariates there is at present no shape representation method which allows to precisely represent the shapes while capturing the individual dependencies on each covariate. Such a method would be of high utility to researchers to discover knowledge hidden in a population of shapes. For scientific shape discovery purpose, we propose a 3D Neural Additive Model for Interpretable Shape Representation ($\texttt{NAISR}$) which describes individual shapes by deforming a shape atlas in accordance to the effect of disentangled covariates. Our approach captures shape population trends and allows for patient-specific predictions through shape transfer. $\texttt{NAISR}$ is the first approach to combine the benefits of deep implicit shape representations with an atlas deforming according to specified covariates. We evaluate $\texttt{NAISR}$ with respect to shape reconstruction, shape disentanglement, shape evolution, and shape transfer on three datasets, i.e. 1) $\textit{Starman}$, a simulated 2D shape dataset; 2) ADNI hippocampus 3D shape dataset; 3) pediatric airway 3D shape dataset. Our experiments demonstrate that $\texttt{NAISR}$ achieves competitive shape reconstruction performance while retaining interpretability. Our code is available at https://github.com/uncbiag/NAISR.

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YNICL Journal 2019 Journal Article

Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson's disease

Bin Xiao
Naying He
Qian Wang
Zenghui Cheng
Yining Jiao
E. Mark Haacke
Fuhua Yan
Feng Shi

Parkinson's disease is the second most common neurodegenerative disease in the elderly after Alzheimer's disease. The aetiology and pathogenesis of Parkinson's disease (PD) are still unclear, but the loss of dopaminergic cells and the excessive iron deposition in the substantia nigra (SN) are associated with the pathophysiology. As an imaging technique that can quantitatively reflect the amount of iron deposition, Quantitative Susceptibility Mapping (QSM) has been shown to be a promising modality for the diagnosis of PD. In the present work, we propose a hybrid feature extraction method for PD diagnosis using QSM images. First, we extract radiomics features from the SN using QSM and employ machine learning algorithms to classify PD and normal controls (NC). This approach allows us to investigate which features are most vulnerable to the effects of the disease. Along with this approach, we propose a Convolutional Neural Network (CNN) based method which can extract different features from the QSM image to further support the diagnosis of PD. Finally, we combine these two types of features and we find that the radiomics features and CNN features are complementary to each other, which helps further improve the classification (diagnostic) performance. We conclude that: (1) radiomics features from QSM data have significant clinical value for the diagnosis of PD; (2) CNN features are also useful in the diagnosis of PD; and (3) the combination of radiomics features and CNN features can enhance the diagnostic accuracy.

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