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Hui Tang

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12 papers

2 author rows

ICML Conference 2025 Conference Paper

Concept-Based Unsupervised Domain Adaptation

Xinyue Xu
Yueying Hu
Hui Tang
Yi Qin 0004
Lu Mi
Hao Wang 0014
Xiaomeng Li 0001

Concept Bottleneck Models (CBMs) enhance interpretability by explaining predictions through human-understandable concepts but typically assume that training and test data share the same distribution. This assumption often fails under domain shifts, leading to degraded performance and poor generalization. To address these limitations and improve the robustness of CBMs, we propose the Concept-based Unsupervised Domain Adaptation (CUDA) framework. CUDA is designed to: (1) align concept representations across domains using adversarial training, (2) introduce a relaxation threshold to allow minor domain-specific differences in concept distributions, thereby preventing performance drop due to over-constraints of these distributions, (3) infer concepts directly in the target domain without requiring labeled concept data, enabling CBMs to adapt to diverse domains, and (4) integrate concept learning into conventional domain adaptation (DA) with theoretical guarantees, improving interpretability and establishing new benchmarks for DA. Experiments demonstrate that our approach significantly outperforms the state-of-the-art CBM and DA methods on real-world datasets.

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ECAI Conference 2025 Conference Paper

IBS-Net: Advancing Implicit Boundary-Aware Segmentation for Diaphragm Ultrasound Analysis

Baike Shi
Yikang He
Chenlong Miao
Wenbo Huang
Tao Wang 0107
Tianyi Liu
Hui Tang
Jianmin Dong

Accurate automated measurement of diaphragmatic thickness in ultrasound imaging is a critical challenging task for respiratory function assessment, primarily due to difficulties in precise fascial identification. And ultrasound visualization of the diaphragm is characterized by unique challenges, including discontinuous and blurred boundary delineations caused by imaging artifacts, as well as interference and influence from adjacent muscular reverberations. These problems are further compounded by subjects’ pose variations during image acquisition. To address these challenges, we introduce IBS-Net, an innovative triple-branch interactive segmentation network that synergistically combines boundary regression with auxiliary task learning to optimize feature representation in segmentation task. Moreover, Our framework incorporates two innovative module: an Adaptive Fusion Module (AFM) that enables multi-scale hierarchical feature refinement for precise boundary characterization, and a Cross Interactive Module (CIM) that employs parallel-encoded feature extraction to simultaneously achieve accurate fascial localization while preserving structural topology. These complementary mechanisms effectively resolve spatial feature inconsistencies, facilitating robust multi-level feature integration. Comprehensive experimental results demonstrate that IBS-Net achieves statistically significant improvements of 8. 9% in Dice similarity coefficient and 8. 05% in Jaccard index compared to conventional methods. Moreover, to verify the effectiveness of the proposed method, we extended it to other publicly available BUSI dataset for experimentation. The results demonstrate that our method is competitive in terms of both accuracy and completeness in the identification of fuzzy boundaries in ultrasound images.

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EAAI Journal 2025 Journal Article

Multisource aerodynamic data reconstruction method using an enhanced multifidelity neural network

Xu Wang
Huailu Li
Haitao Lin
Hui Tang
Weiwei Zhang

The acquisition of aircraft aerodynamic pressure distribution typically relies on wind tunnel tests or numerical simulations. However, discrepancies in accuracy and efficiency between multisource data present challenges for aerodynamic data reconstruction. In the present work, we propose a novel multifidelity architecture to enhance the model’s applicability to nonlinear inconsistency problems commonly encountered in transonic aerodynamic problems. By incorporating difference operations into the multifidelity neural network, the model can adaptively find suitable mapping relationships from potential low fidelity data. This method can reduce modeling errors when there is a trend inconsistencies between high and low fidelity data, which is a challenge that traditional multifidelity models struggle to address. To demonstrate the efficiency of our proposed ideas, we conducted multifidelity modeling on classic numerical examples and aerodynamic cases. Predictive results indicate that inconsistencies between multifidelity data can significantly affect traditional models, whereas the proposed multifidelity approach can enhance generalization performance. The reconstruction results of transonic pressure distribution for airfoils and the Office National d’Études et de Recherches Aérospatiales (ONERA) M6 wing indicate that the enhanced multifidelity model can effectively capture shock wave locations, thereby improving modeling accuracy. Analysis of the reconstruction results for pressure distribution indicates that the proposed method can reduce reconstruction error by over 30% compared to deep neural networks and multifidelity neural network. This method is also applicable for the data fusion of experimental and simulation data in various engineering problems.

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YNIMG Journal 2025 Journal Article

Structural damage-driven brain compensation among near-centenarians and centenarians without dementia

Hui Tang
Haichao Zhao
Hao Liu
Jiyang Jiang
Nicole Kochan
Jing Jing
Henry Brodaty
Wei Wen

Compensation has been proposed as a mechanism to explain how individuals in very old age remain able to maintain normal cognitive functioning. Previous studies have provided evidence on the role of increasing functional connectivity as a compensatory mechanism for age-related white matter damage. However, we lack direct investigation into how these mechanisms contribute to the preservation of cognition in the very old population. We examined a cohort of near-centenarians and centenarians without dementia (aged 95-103 years, n=44). We constructed a structural disconnection matrix based on the disruption of white matter pathways caused by white matter hyperintensities (WMHs), aiming to explore the relationship between functional connections, cognitive preservation and white matter damage. Our results revealed that structural damage can reliably explain the variations of functional connections or cognitive maintenance. Notably, we found significant correlations between the weights in the functional connectivity model and the weights in the cognition model. We observed positive correlations between models for brain disconnections and cognitive function in near-centenarians and centenarians. The strongest effects were found between attention and somatomotor network (SMN) (r=0.397, p<0.001), memory and SMN (r=0.333 p<0.001), fluency and visual network (VIS) - control network (CN) (r=0.406, p<0.001), language and VIS (r=0.309, p<0.001), visuospatial ability and VIS-default mode network (DMN) (r=0.464, p<0.001), as well as global cognition and VIS-DMN (r=0.335, p<0.001). These findings suggest that enhancement of functional connectivity may serve as a compensatory mechanism, such that it mitigates the effects of white matter damage and contributes to preserved cognitive performance in very old age.

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AAAI Conference 2024 Short Paper

Graph Anomaly Detection via Prototype-Aware Label Propagation (Student Abstract)

Hui Tang
Xun Liang
Sensen Zhang

Detecting anomalies on attributed graphs is a challenging task since labelled anomalies are highly labour-intensive by taking specialized domain knowledge to make anomalous samples not as available as normal ones. Moreover, graphs contain complex structure information as well as attribute information, leading to anomalies that can be typically hidden in the structure space, attribute space, and the mix of both. In this paper, we propose a novel model for graph anomaly detection named ProGAD. Specifically, ProGAD takes advance of label propagation to infer high-quality pseudo labels by considering the structure and attribute inconsistencies between normal and abnormal samples. Meanwhile, ProGAD introduces the prior knowledge of class distribution to correct and refine pseudo labels with a prototype-aware strategy. Experiments demonstrate that ProGAD achieves strong performance compared with the current state-of-the-art methods.

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JBHI Journal 2024 Journal Article

LeSAM: Adapt Segment Anything Model for Medical Lesion Segmentation

Yunbo Gu
Qianyu Wu
Hui Tang
Xiaoli Mai
Huazhong Shu
Baosheng Li
Yang Chen

The Segment Anything Model (SAM) is a foundational model that has demonstrated impressive results in the field of natural image segmentation. However, its performance remains suboptimal for medical image segmentation, particularly when delineating lesions with irregular shapes and low contrast. This can be attributed to the significant domain gap between medical images and natural images on which SAM was originally trained. In this paper, we propose an adaptation of SAM specifically tailored for lesion segmentation termed LeSAM. LeSAM first learns medical-specific domain knowledge through an efficient adaptation module and integrates it with the general knowledge obtained from the pre-trained SAM. Subsequently, we leverage this merged knowledge to generate lesion masks using a modified mask decoder implemented as a lightweight U-shaped network design. This modification enables better delineation of lesion boundaries while facilitating ease of training. We conduct comprehensive experiments on various lesion segmentation tasks involving different image modalities such as CT scans, MRI scans, ultrasound images, dermoscopic images, and endoscopic images. Our proposed method achieves superior performance compared to previous state-of-the-art methods in 8 out of 12 lesion segmentation tasks while achieving competitive performance in the remaining 4 datasets. Additionally, ablation studies are conducted to validate the effectiveness of our proposed adaptation modules and modified decoder.

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JBHI Journal 2022 Journal Article

Masked Joint Bilateral Filtering via Deep Image Prior for Digital X-Ray Image Denoising

Qianyu Wu
Hui Tang
Hanxi Liu
Yang Chen Chen

Medical image denoising faces great challenges. Although deep learning methods have shown great potential, their efficiency is severely affected by millions of trainable parameters. The non-linearity of neural networks also makes them difficult to be understood. Therefore, existing deep learning methods have been sparingly applied to clinical tasks. To this end, we integrate known filtering operators into deep learning and propose a novel Masked Joint Bilateral Filtering (MJBF) via deep image prior for digital X-ray image denoising. Specifically, MJBF consists of a deep image prior generator and an iterative filtering block. The deep image prior generator produces plentiful image priors by a multi-scale fusion network. The generated image priors serve as the guidance for the iterative filtering block, which is utilized for the actual edge-preserving denoising. The iterative filtering block contains three trainable Joint Bilateral Filters (JBFs), each with only 18 trainable parameters. Moreover, a masking strategy is introduced to reduce redundancy and improve the understanding of the proposed network. Experimental results on the ChestX-ray14 dataset and real data show that the proposed MJBF has achieved superior performance in terms of noise suppression and edge preservation. Tests on the portability of the proposed method demonstrate that this denoising modality is simple yet effective, and could have a clinical impact on medical imaging in the future.

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YNIMG Journal 2021 Journal Article

A slower rate of sulcal widening in the brains of the nondemented oldest old

Hui Tang
Tao Liu
Hao Liu
Jiyang Jiang
Jian Cheng
Haijun Niu
Shuyu Li
Henry Brodaty

The relationships between aging and brain morphology have been reported in many previous structural brain studies. However, the trajectories of successful brain aging in the extremely old remain underexplored. In the limited research on the oldest old, covering individuals aged 85 years and older, there are very few studies that have focused on the cortical morphology, especially cortical sulcal features. In this paper, we measured sulcal width and depth as well as cortical thickness from T1-weighted scans of 290 nondemented community-dwelling participants aged between 76 and 103 years. We divided the participants into young old (between 76 and 84; mean = 80.35±2.44; male/female = 76/88) and oldest old (between 85 and 103; mean = 91.74±5.11; male/female = 60/66) groups. The results showed that most of the examined sulci significantly widened with increased age and that the rates of sulcal widening were lower in the oldest old. The spatial pattern of the cortical thinning partly corresponded with that of sulcal widening. Compared to females, males had significantly wider sulci, especially in the oldest old. This study builds a foundation for future investigations of neurocognitive disorders and neurodegenerative diseases in the oldest old, including centenarians.

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AAAI Conference 2020 Conference Paper

Discriminative Adversarial Domain Adaptation

Hui Tang
Kui Jia

Given labeled instances on a source domain and unlabeled ones on a target domain, unsupervised domain adaptation aims to learn a task classiﬁer that can well classify target instances. Recent advances rely on domain-adversarial training of deep networks to learn domain-invariant features. However, due to an issue of mode collapse induced by the separate design of task and domain classiﬁers, these methods are limited in aligning the joint distributions of feature and category across domains. To overcome it, we propose a novel adversarial learning method termed Discriminative Adversarial Domain Adaptation (DADA). Based on an integrated category and domain classiﬁer, DADA has a novel adversarial objective that encourages a mutually inhibitory relation between category and domain predictions for any input instance. We show that under practical conditions, it deﬁnes a minimax game that can promote the joint distribution alignment. Except for the traditional closed set domain adaptation, we also extend DADA for extremely challenging problem settings of partial and open set domain adaptation. Experiments show the efﬁcacy of our proposed methods and we achieve the new state of the art for all the three settings on benchmark datasets.

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AAAI Conference 2020 Conference Paper

Object-Guided Instance Segmentation for Biological Images

Jingru Yi
Hui Tang
Pengxiang Wu
Bo Liu
Daniel J. Hoeppner
Dimitris N. Metaxas
Lianyi Han
Wei Fan

Instance segmentation of biological images is essential for studying object behaviors and properties. The challenges, such as clustering, occlusion, and adhesion problems of the objects, make instance segmentation a non-trivial task. Current box-free instance segmentation methods typically rely on local pixel-level information. Due to a lack of global object view, these methods are prone to over- or undersegmentation. On the contrary, the box-based instance segmentation methods incorporate object detection into the segmentation, performing better in identifying the individual instances. In this paper, we propose a new box-based instance segmentation method. Mainly, we locate the object bounding boxes from their center points. The object features are subsequently reused in the segmentation branch as a guide to separate the clustered instances within an RoI patch. Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects. Consequently, the proposed model performs excellently in segmenting the clustered objects while retaining the target object details. The proposed method achieves state-of-the-art performances on three biological datasets: cell nuclei, plant phenotyping dataset, and neural cells.

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AAAI Conference 2020 Short Paper

Self-Supervised, Semi-Supervised, Multi-Context Learning for the Combined Classification and Segmentation of Medical Images (Student Abstract)

Abdullah-Al-Zubaer Imran
Chao Huang
Hui Tang
Wei Fan
Yuan Xiao
Dingjun Hao
Zhen Qian
Demetri Terzopoulos

To tackle the problem of limited annotated data, semisupervised learning is attracting attention as an alternative to fully supervised models. Moreover, optimizing a multipletask model to learn “multiple contexts” can provide better generalizability compared to single-task models. We propose a novel semi-supervised multiple-task model leveraging selfsupervision and adversarial training—namely, self-supervised, semi-supervised, multi-context learning (S4 MCL)—and apply it to two crucial medical imaging tasks, classiﬁcation and segmentation. Our experiments on spine X-rays reveal that the S4 MCL model signiﬁcantly outperforms semisupervised single-task, semi-supervised multi-context, and fully-supervised single-task models, even with a 50% reduction of classiﬁcation and segmentation labels.

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AAAI Conference 2020 Conference Paper

Shape-Aware Organ Segmentation by Predicting Signed Distance Maps

Yuan Xue
Hui Tang
Zhi Qiao
Guanzhong Gong
Yong Yin
Zhen Qian
Chao Huang
Wei Fan

In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall shape of the target organ and often lack smoothness. Since there is a rigorous mapping between the Signed Distance Map (SDM) calculated from object boundary contours and the binary segmentation map, we exploit the feasibility of learning the SDM directly from medical scans. By converting the segmentation task into predicting an SDM, we show that our proposed method retains superior segmentation performance and has better smoothness and continuity in shape. To leverage the complementary information in traditional segmentation training, we introduce an approximated Heaviside function to train the model by predicting SDMs and segmentation maps simultaneously. We validate our proposed models by conducting extensive experiments on a hippocampus segmentation dataset and the public MICCAI 2015 Head and Neck Auto Segmentation Challenge dataset with multiple organs. While our carefully designed backbone 3D segmentation network improves the Dice coefﬁcient by more than 5% compared to current state-of-the-arts, the proposed model with SDM learning produces smoother segmentation results with smaller Hausdorff distance and average surface distance, thus proving the effectiveness of our method.

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