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Da He

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3 papers

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JBHI Journal 2026 Journal Article

Predicting the Effort Required to Manually Mend Auto-Segmentations

Da He
Yubing Tong
Drew A. Torigian
Jayaram K. Udupa

Auto-segmentation quality or accuracy influences their clinical usefulness. However, currently widely utilized segmentation metrics (e. g. , Dice Coefficient ( DC ) and Hausdorff Distance ( HD )) cannot effectively express the manual mending effort required when utilizing auto-segmentation results in clinical practice. In this article, we explore ways of evaluating auto-segmentations with clinical efficiency considerations in mind. The time required for correcting auto-segmentations by experts is recorded to indicate ground-truth mending effort. Extended from our previous work, five explicitly-defined metrics are studied in detail for their ability to predict mending effort. More importantly, we explore the use of deep learning networks to provide an implicit metric, which predict mending effort using auto-segmentation masks and original images as input. A 3-institution evaluation is conducted with 7 different anatomic organs in the setting of auto-contouring for radiation therapy planning. Among the five explicit metrics, one form of the proposed Mendability Index ( MIhd ) shows the best performance to indicate the mending effort for sparse objects with 6. 2-14. 4% error, while one form of HD ( sHD ) performs best when assessing large non-sparse objects. Interestingly, while the explicit metrics all require ground truth segmentations for estimating mending effort, the implicit models obtained via deep learning are effective in predicting mending efforts (with 2. 9-12. 9% error) without the need for ground-truth segmentations and directly from the given image plus the auto-segmentations. We conclude that once effort-predicting deep models are created, it is feasible to assess the clinical usability of new segmentation models, going beyond bench technical evaluation commonly done via explicit metrics.

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JBHI Journal 2025 Journal Article

Automatic Multi-Task Segmentation and Vulnerability Assessment of Carotid Plaque on Contrast-Enhanced Ultrasound Images and Videos via Deep Learning

Bokai Hu
Han Zhang
Caixia Jia
Ke Chen
Xiangjiang Tang
Da He
Luni Zhang
Shiyao Gu

Intraplaque neovascularization (IPN) within carotid plaque is a crucial indicator of plaque vulnerability. Contrast-enhanced ultrasound (CEUS) is a valuable tool for assessing IPN by evaluating the location and quantity of microbubbles within the carotid plaque. However, this task is typically performed by experienced radiologists. Here we propose a deep learning-based multi-task model for the automatic segmentation and IPN grade classification of carotid plaque on CEUS images and videos. We also compare the performance of our model with that of radiologists. To simulate the clinical practice of radiologists, who often use CEUS videos with dynamic imaging to track microbubble flow and identify IPN, we develop a workflow for plaque vulnerability assessment using CEUS videos. Our multi-task model outperformed individually trained segmentation and classification models, achieving superior performance in IPN grade classification based on CEUS images. Specifically, our model achieved a high segmentation Dice coefficient of 84. 64% and a high classification accuracy of 81. 67%. Moreover, our model surpassed the performance of junior and medium-level radiologists, providing more accurate IPN grading of carotid plaque on CEUS images. For CEUS videos, our model achieved a classification accuracy of 80. 00% in IPN grading. Overall, our multi-task model demonstrates great performance in the automatic, accurate, objective, and efficient IPN grading in both CEUS images and videos. This work holds significant promise for enhancing the clinical diagnosis of plaque vulnerability associated with IPN in CEUS evaluations.

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AAAI Conference 2025 Conference Paper

Global Attribute-Association Pattern Aggregation for Graph Fraud Detection

Mingjiang Duan
Da He
Tongya Zheng
Lingxiang Jia
Mingli Song
Xinyu Wang
Zunlei Feng

Fraud is increasingly prevalent, and its patterns are frequently changing, posing challenges for fraud detection methods such as random forests and Graph Neural Networks (GNNs), which rely on bin-based and mixture features separately. The former may lose crucial graph-associated features, while the latter face incorrect feature fusion. To overcome these limitations, we propose an approach based on attribute-association pattern that leverages the distinct attribute and association patterns differentiating fraudulent from benign behaviors, to enhance fraud detection capabilities. Attribute features are adaptively split into separate bins to eliminate incorrect attribute fusion and combine association patterns through graph neighbor message passing, thereby deriving attribute-association pattern features. Using the learned attribute-association patterns, the fraud patterns between a single pattern and the patterns across the entire graph are globally aggregated. Extensive experiments comparing our approach with 24 methods on 7 datasets demonstrate that the proposed method achieves SOTA performance.

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