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Yi Du

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5 papers

1 author row

EAAI Journal 2025 Journal Article

Myocardial ischemic classification using a knowledge-guided polar transformer in two-dimensional echocardiography

Ziwei Pang
Yi Du
Yanhui Guo
Shuang Chen
Bo Yu
Siqi Guo
Guo-Qing Du

Myocardial ischemia, characterized by inadequate blood supply to the heart muscles, is critical to cardiovascular diseases. Timely and accurate identification of ischemic segments is essential for prompt intervention and patient care. This study developed a Transformer-based model to identify myocardial ischemia in left ventricle short-axis (LVSA) two-dimensional echocardiography (2DE) images where a novel Knowledge-Guided Polar Transformer (KGPT) was proposed that integrated the unique characteristics of 2DE images with the prior clinical knowledge. 305 patients (aged 57. 6 ± 8. 8 years) were selected and underwent transthoracic echocardiography within 1–3 days prior to invasive coronary angiography (ICA). With ICA and quantitative flow ratio as the gold standard of myocardial ischemia, the KGPT model was trained to classify the LVSA 2DE images as ischemia or non-ischemia by capturing spatial features in a radial orientation. Its performance was evaluated with five-fold cross-validation and receiver operating characteristic curve (ROC) analysis. It achieved an area under ROC (AUC) of 0. 8326 ± 0. 0906, with an accuracy of 79. 50 ± 5. 40 %, precision of 79. 07 ± 6. 70 %, recall of 80. 79 ± 7. 87 %, and F1 score of 78. 43 ± 6. 56 %. In comparison, the original Swin-Transformer model produced an AUC of 0. 7011 ± 0. 0334, accuracy of 70. 20 ± 1. 04 %, precision of 68. 58 ± 3. 12 %, recall of 63. 21 ± 3. 60 %, and F1 score of 63. 13 ± 3. 78 %. The differences were statistically significant (P < 0. 05). The KGPT also demonstrated significantly superior performance to radiologists. It effectively classifies ischemic regions in 2DE images, presenting a promising tool for diagnosing myocardial ischemia. The integration of clinical knowledge with Transformer enhances the accuracy and reliability of ischemia classification, potentially revolutionizing the diagnosis and monitoring of myocardial ischemic diseases.

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YNIMG Journal 2024 Journal Article

Unveiling the core functional networks of cognition: An ontology-guided machine learning approach

Guowei Wu
Zaixu Cui
Xiuyi Wang
Yi Du

Deciphering the functional architecture that underpins diverse cognitive functions is fundamental quest in neuroscience. In this study, we employed an innovative machine learning framework that integrated cognitive ontology with functional connectivity analysis to identify brain networks essential for cognition. We identified a core assembly of functional connectomes, primarily located within the association cortex, which showed superior predictive performance compared to two conventional methods widely employed in previous research across various cognitive domains. Our approach achieved a mean prediction accuracy of 0.13 across 16 cognitive tasks, including working memory, reading comprehension, and sustained attention, outperforming the traditional methods' accuracy of 0.08. In contrast, our method showed limited predictive power for sensory, motor, and emotional functions, with a mean prediction accuracy of 0.03 across 9 relevant tasks, slightly lower than the traditional methods' accuracy of 0.04. These cognitive connectomes were further characterized by distinctive patterns of resting-state functional connectivity, structural connectivity via white matter tracts, and gene expression, highlighting their neurogenetic underpinnings. Our findings reveal a domain-general functional network fingerprint that pivotal to cognition, offering a novel computational approach to explore the neural foundations of cognitive abilities.

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YNIMG Journal 2022 Journal Article

Lip movements enhance speech representations and effective connectivity in auditory dorsal stream

Lei Zhang
Yi Du

Viewing speaker's lip movements facilitates speech perception, especially under adverse listening conditions, but the neural mechanisms of this perceptual benefit at the phonemic and feature levels remain unclear. This fMRI study addressed this question by quantifying regional multivariate representation and network organization underlying audiovisual speech-in-noise perception. Behaviorally, valid lip movements improved recognition of place of articulation to aid phoneme identification. Meanwhile, lip movements enhanced neural representations of phonemes in left auditory dorsal stream regions, including frontal speech motor areas and supramarginal gyrus (SMG). Moreover, neural representations of place of articulation and voicing features were promoted differentially by lip movements in these regions, with voicing enhanced in Broca's area while place of articulation better encoded in left ventral premotor cortex and SMG. Next, dynamic causal modeling (DCM) analysis showed that such local changes were accompanied by strengthened effective connectivity along the dorsal stream. Moreover, the neurite orientation dispersion of the left arcuate fasciculus, the bearing skeleton of auditory dorsal stream, predicted the visual enhancements of neural representations and effective connectivity. Our findings provide novel insight to speech science that lip movements promote both local phonemic and feature encoding and network connectivity in the dorsal pathway and the functional enhancement is mediated by the microstructural architecture of the circuit.

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AAAI Conference 2021 Short Paper

Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion (Student Abstract)

Ziyue Qiao
Zhiyuan Ning
Yi Du
Yuanchun Zhou

Most researches for knowledge graph completion learn representations of entities and relations to predict missing links in incomplete knowledge graphs. However, these methods fail to take full advantage of both the contextual information of entity and relation. Here, we extract contexts of entities and relations from the triplets which they compose. We propose a model named AggrE, which conducts efficient aggregations respectively on entity context and relation context in multihops, and learns context-enhanced entity and relation embeddings for knowledge graph completion. The experiment results show that AggrE is competitive to existing models.

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TIST Journal 2021 Journal Article

TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution

Fayaz Ali Dharejo
Farah Deeba
Yuanchun Zhou
Bhagwan Das
Munsif Ali Jatoi
Muhammad Zawish
Yi Du
Xuezhi Wang

Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and generative adversarial networks (GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR). However, the generated image still suffers from undesirable artifacts such as the absence of texture-feature representation and high-frequency information. We propose a frequency domain-based spatio-temporal remote sensing single image super-resolution technique to reconstruct the HR image combined with generative adversarial networks (GANs) on various frequency bands (TWIST-GAN). We have introduced a new method incorporating Wavelet Transform (WT) characteristics and transferred generative adversarial network. The LR image has been split into various frequency bands by using the WT, whereas the transfer generative adversarial network predicts high-frequency components via a proposed architecture. Finally, the inverse transfer of wavelets produces a reconstructed image with super-resolution. The model is first trained on an external DIV2 K dataset and validated with the UC Merced Landsat remote sensing dataset and Set14 with each image size of 256 × 256. Following that, transferred GANs are used to process spatio-temporal remote sensing images in order to minimize computation cost differences and improve texture information. The findings are compared qualitatively and qualitatively with the current state-of-art approaches. In addition, we saved about 43% of the GPU memory during training and accelerated the execution of our simplified version by eliminating batch normalization layers.

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