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Zailiang Chen

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AAAI Conference 2026 Conference Paper

A Pseudo-Label Optimization Method Based on Polar Coordinate Modeling and Prior Constraints

Yudi Wang
Hailan Shen
Yixiao Fu
Yuqi Li
Zeshi Lu
Zailiang Chen

Magnetic Resonance Imaging (MRI) and its automatic segmentation are pivotal in assisting physicians with clinical diagnosis. In recent years, with the scarcity of labeled data, significant advancements have been made in semi-supervised segmentation. However, the prediction of many current methods is affected by the presence of false positive regions, which limits their reliability in clinical applications. To tackle this issue, we propose a pseudo-label optimization method based on polar coordinate modeling and prior constraints (PMPC), which refines false positive regions in pseudo-labels by leveraging prior knowledge within the polar coordinate system. Firstly, to improve the efficiency and rationality during polar coordinate modeling, the Adaptive Pole Selection (APS) algorithm is presented to ensure that the pole is located within the foreground region. Secondly, to mitigate false positive regions in pseudo-labels that violate medical anatomical priors, we propose the Prior Knowledge Constraint in Polar Coordinate System (KCP) module to reassign pixel categories in these regions. Finally, the Shape-aware Weighting (SaW) strategy is presented to evaluate the quality of the optimized pseudo-labels based on their shape and then determine their weight in guiding network parameter updates. Experiments on three MRI datasets demonstrate that the proposed method can be effectively integrated with existing pelvic MRI segmentation approaches, significantly reducing false positive rates and further improving segmentation quality.

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JBHI Journal 2020 Journal Article

Direct Cup-to-Disc Ratio Estimation for Glaucoma Screening via Semi-Supervised Learning

Rongchang Zhao
Xuanlin Chen
Xiyao Liu
Zailiang Chen
Fan Guo
Shuo Li

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The Cup-to-Disc Ratio (CDR) serves as the most important indicator for glaucoma screening and plays a significant role in clinical screening and early diagnosis of glaucoma. In general, obtaining CDR is subjected to measuring on manually or automatically segmented optic disc and cup. Despite great efforts have been devoted, obtaining CDR values automatically with high accuracy and robustness is still a great challenge due to the heavy overlap between optic cup and neuroretinal rim regions. In this paper, a direct CDR estimation method is proposed based on the well-designed semi-supervised learning scheme, in which CDR estimation is formulated as a general regression problem while optic disc/cup segmentation is cancelled. The method directly regresses CDR value based on the feature representation of optic nerve head via deep learning technique while bypassing intermediate segmentation. The scheme is a two-stage cascaded approach comprised of two phases: unsupervised feature representation of fundus image with a convolutional neural networks (MFPPNet) and CDR value regression by random forest regressor. The proposed scheme is validated on the challenging glaucoma dataset Direct-CSU and public ORIGA, and the experimental results demonstrate that our method can achieve a lower average CDR error of 0. 0563 and a higher correlation of around 0. 726 with measurement before manual segmentation of optic disc/cup by human experts. Our estimated CDR values are also tested for glaucoma screening, which achieves the areas under curve of 0. 905 on dataset of 421 fundus images. The experiments show that the proposed method is capable of state-of-the-art CDR estimation and satisfactory glaucoma screening with calculated CDR value.

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AAAI Conference 2019 Conference Paper

Weakly-Supervised Simultaneous Evidence Identification and Segmentation for Automated Glaucoma Diagnosis

Rongchang Zhao
Wangmin Liao
Beiji Zou
Zailiang Chen
Shuo Li

Evidence identification, optic disc segmentation and automated glaucoma diagnosis are the most clinically significant tasks for clinicians to assess fundus images. However, delivering the three tasks simultaneously is extremely challenging due to the high variability of fundus structure and lack of datasets with complete annotations. In this paper, we propose an innovative Weakly-Supervised Multi-Task Learning method (WSMTL) for accurate evidence identification, optic disc segmentation and automated glaucoma diagnosis. The WSMTL method only uses weak-label data with binary diagnostic labels (normal/glaucoma) for training, while obtains pixel-level segmentation mask and diagnosis for testing. The WSMTL is constituted by a skip and densely connected CNN to capture multi-scale discriminative representation of fundus structure; a well-designed pyramid integration structure to generate high-resolution evidence map for evidence identification, in which the pixels with higher value represent higher confidence to highlight the abnormalities; a constrained clustering branch for optic disc segmentation; and a fully-connected discriminator for automated glaucoma diagnosis. Experimental results show that our proposed WSMTL effectively and simultaneously delivers evidence identification, optic disc segmentation (89. 6% TP Dice), and accurate glaucoma diagnosis (92. 4% AUC). This endows our WSMTL a great potential for the effective clinical assessment of glaucoma.

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