arXiv subjects

Zhengyong Huang

Publications and source records attributed to Zhengyong Huang.

4 recordsLinked to original sources

arXiv2026

Revisiting Shape and Texture Reliance with Category-Separability-Calibrated Suppression

Feature-suppression evaluations infer model reliance on shape or texture from the accuracy loss caused by attenuating each type of information. Such losses, however, conflate feature reliance with the amount of category-relevant information removed by the corresponding transformation. Because shape and texture are suppressed using different operators, their effects are not directly comparable. We introduce the Semantic Degradation Index (SDI), which quantifies the suppression-induced reduction in category separability relative to clean images in a fixed clean-reference discriminative space constructed from handcrafted features. On an ImageNet16-like benchmark, we use SDI to compare Gaussian blur for texture suppression with grid distortion for shape suppression over their overlapping degradation range. At comparable SDI values, all five evaluated ImageNet-trained convolutional neural networks (CNNs) retain less accuracy under Gaussian blur than under grid distortion. This results supports stronger texture than shape reliance under the evaluated operators, contrasting with the shape-dominant conclusion obtained from unmatched suppression conditions. The evaluated Vision Transformers (ViTs) also generally retain more accuracy than CNNs under both operators. To determine whether this advantage extends beyond classification, We evaluate fixed brain-encoding models using clean and suppressed images from the Natural Scenes Dataset. Under both operators, ViT features show smaller suppression-induced decreases in noise-ceiling-normalized explained variance than CNN features. These findings establish category separability as an important reference for interpreting suppression-based feature reliance and show that the CNN-ViT robustness difference extends to model representations predictive of human visual cortical responses.

cs.CV↗

arXiv2026

Partial Decoder Attention Network with Contour-weighted Loss Function for Data-Imbalance Medical Image Segmentation

Image segmentation is pivotal in medical image analysis, facilitating clinical diagnosis, treatment planning, and disease evaluation. Deep learning has significantly advanced automatic segmentation methodologies by providing superior modeling capability for complex structures and fine-grained anatomical regions. However, medical images often suffer from data imbalance issues, such as large volume disparities among organs or tissues, and uneven sample distributions across different anatomical structures. This imbalance tends to bias the model toward larger organs or more frequently represented structures, while overlooking smaller or less represented structures, thereby affecting the segmentation accuracy and robustness. To address these challenges, we proposed a novel contour-weighted segmentation approach, which improves the model's capability to represent small and underrepresented structures. We developed PDANet, a lightweight and efficient segmentation network based on a partial decoder mechanism. We evaluated our method using three prominent public datasets. The experimental results show that our methodology excelled in three distinct tasks: segmenting multiple abdominal organs, brain tumors, and pelvic bone fragments with injuries. It consistently outperformed nine state-of-the-art methods. Moreover, the proposed contour-weighted strategy improved segmentation for other comparison methods across the three datasets, yielding average enhancements in Dice scores of 2.32%, 1.67%, and 3.60%, respectively. These results demonstrate that our contour-weighted segmentation method surpassed current leading approaches in both accuracy and robustness. As a model-independent strategy, it can seamlessly fit various segmentation frameworks, enhancing their performance. This flexibility highlighted its practical importance and potential for broad use in medical image analysis.

eess.IV↗

arXiv2026

Unsupervised Deformable Image Registration with Local-Global Attention and Image Decomposition

Deformable image registration is a critical technology in medical image analysis, with broad applications in clinical practice such as disease diagnosis, multi-modal fusion, and surgical navigation. Traditional methods often rely on iterative optimization, which is computationally intensive and lacks generalizability. Recent advances in deep learning have introduced attention-based mechanisms that improve feature alignment, yet accurately registering regions with high anatomical variability remains challenging. In this study, we proposed a novel unsupervised deformable image registration framework, LGANet++, which employs a novel local-global attention mechanism integrated with a unique technique for feature interaction and fusion to enhance registration accuracy, robustness, and generalizability. We evaluated our approach using five publicly available datasets, representing three distinct registration scenarios: cross-patient, cross-time, and cross-modal CT-MR registration. The results demonstrated that our approach consistently outperforms several state-of-the-art registration methods, improving registration accuracy by 1.39% in cross-patient registration, 0.71% in cross-time registration, and 6.12% in cross-modal CT-MR registration tasks. These results underscore the potential of LGANet++ to support clinical workflows requiring reliable and efficient image registration. The source code is available at https://github.com/huangzyong/LGANet-Registration.

eess.IV↗

arXiv2022

ISA-Net: Improved spatial attention network for PET-CT tumor segmentation

Achieving accurate and automated tumor segmentation plays an important role in both clinical practice and radiomics research. Segmentation in medicine is now often performed manually by experts, which is a laborious, expensive and error-prone task. Manual annotation relies heavily on the experience and knowledge of these experts. In addition, there is much intra- and interobserver variation. Therefore, it is of great significance to develop a method that can automatically segment tumor target regions. In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information. In addition, our network uses dual-channel inputs in the coding stage and fuses them in the decoding stage, which can take advantage of the differences and complementarities between PET and CT. We validated the proposed ISA-Net method on two clinical datasets, a soft tissue sarcoma(STS) and a head and neck tumor(HECKTOR) dataset, and compared with other attention methods for tumor segmentation. The DSC score of 0.8378 on STS dataset and 0.8076 on HECKTOR dataset show that ISA-Net method achieves better segmentation performance and has better generalization. Conclusions: The method proposed in this paper is based on multi-modal medical image tumor segmentation, which can effectively utilize the difference and complementarity of different modes. The method can also be applied to other multi-modal data or single-modal data by proper adjustment.

eess.IV↗