Deep Dual Attention Network for Precise Diagnosis of COVID-19 From Chest CT Images

Lin, Zhijie; He, Zhaoshui; Yao, Ruoyu; Wang, Xu; Liu, Taiheng; Deng, Yamei; Xie, Shengli

doi:10.1109/tai.2022.3225372

Cited by 26 publications

(8 citation statements)

References 49 publications

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“…Inspired by the attention strategy proposed in Ref. [ 34 ] with ResNet for scene image segmentation and several segmentation approaches that followed this attention mechanism [ 35 – 37 ], we proposed an ADA module (green and orange flow operations shown as the ADA module in Fig. 1 ) in our encoder-decoder-based segmentation model to improve the segmentation performance for the organoid images.…”

Section: Methodsmentioning

confidence: 99%

Deep learning based characterization of human organoids using optical coherence tomography

Wang,

Ganjee,

Khandaker

et al. 2024

Biomed. Opt. Express

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Organoids, derived from human induced pluripotent stem cells (hiPSCs), are intricate three-dimensional in vitro structures that mimic many key aspects of the complex morphology and functions of in vivo organs such as the retina and heart. Traditional histological methods, while crucial, often fall short in analyzing these dynamic structures due to their inherently static and destructive nature. In this study, we leveraged the capabilities of optical coherence tomography (OCT) for rapid, non-invasive imaging of both retinal, cerebral, and cardiac organoids. Complementing this, we developed a sophisticated deep learning approach to automatically segment the organoid tissues and their internal structures, such as hollows and chambers. Utilizing this advanced imaging and analysis platform, we quantitatively assessed critical parameters, including size, area, volume, and cardiac beating, offering a comprehensive live characterization and classification of the organoids. These findings provide profound insights into the differentiation and developmental processes of organoids, positioning quantitative OCT imaging as a potentially transformative tool for future organoid research.

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Section: Methodsmentioning

confidence: 99%

Deep learning based characterization of human organoids using optical coherence tomography

Wang,

Ganjee,

Khandaker

et al. 2024

Biomed. Opt. Express

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show abstract

“…This innovative design efficiently extracts features with limited parameters, intricately fuses multiscale semantic features, and successfully restores rich details through a unique attention mechanism, significantly improving image segmentation performance. Lin et al [21] introduced a deep dual attention network (D2ANet) for COVID-19 diagnosis using chest CT images, skillfully integrating dual attention modules(DAM) and multi-scale feature extractors to automatically detect lesion areas and extract [25]. While dilated convolutions and atention mechanisms can enhance the performance of segmentation netwo-rks, they still have some limitations.…”

Section: Related Workmentioning

confidence: 99%

DDRA-Net: Dual-Channel Deep Residual Attention UPerNet for Breast Lesions Segmentation in Ultrasound Images

Sun,

Yuan,

Tian

et al. 2024

IEEE Access

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Automated segmentation of breast tumors in breast ultrasound images has been a challenging frontier issue. The morphological diversity, boundary ambiguity, and heterogeneity of malignant tumors in breast lesions constrain the improvement of segmentation accuracy. To address these challenges, we propose an innovative deep learning-based method, namely Dual-Channel Deep Residual Attention UPerNet (DDRA-net), for efficient and accurate segmentation of breast tumor regions. The core of DDRAnet lies in the Dual-Channel Deep Residual Attention module (DDRA), which integrates depth-wise separable convolution and Convolutional Block Attention Module (CBAM). This design aims to enhance the extraction of crucial features within the receptive field to better capture subtle details of breast lesions. Through extensive experimental evaluation, DDRA-net demonstrates remarkable performance on a publicly available breast ultrasound datasets, exhibiting higher segmentation accuracy and stability compared to contemporary mainstream deep learning methods. Importantly, it is worth emphasizing that the flexibility of this method allows easy integration with other network structures to further improve the performance and applicability of breast tumor segmentation. In the segmentation of the Breast Ultrasound Image dataset , our precision, recall, IoU, F1 score, Dice, and Hausdorff Distance achieved the following values: 95.31%, 90.79%, 88.00%, 92.39%, 95.46%, and 3.02, respectively. Compared to the original UPerNet, DDRA-net demonstrated improvements of 2.92%, 4.64%, 5.52%, 4.97%, 3.4%, and 24.5% in these six metrics on the Breast Ultrasound Image dataset.

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“…Inspired by the attention strategy proposed in Ref. [34] with ResNet for scene image segmentation and several segmentation approaches that followed this attention mechanism [35][36][37], we proposed an ADA module (green and orange flow operations shown as the ADA module in Fig. 1) in our encoder-decoder-based segmentation model to improve the segmentation performance for the organoid images.…”

Section: Ada Modulementioning

confidence: 99%

Deep learning based characterization of human organoids using optical coherence tomography

Wang,

GANJEE,

KHANDAKER

et al. 2023

Preprint

View full text Add to dashboard Cite

Organoids, derived from human induced pluripotent stem cells (hiPSCs), are intricate three-dimensional in vitro structures that mimic many key aspects of the complex morphology and functions of in vivo organs such as the retina and heart. Traditional histological methods, while crucial, often fall short in analyzing these dynamic structures due to their inherently static and destructive nature. In this study, we leveraged the capabilities of optical coherence tomography (OCT) for rapid, non-invasive imaging of both retinal, cerebral, and cardiac organoids. Complementing this, we developed a sophisticated deep learning approach to automatically segment the organoid tissues and their internal structures, such as hollows and chambers. Utilizing this advanced imaging and analysis platform, we quantitatively assessed critical parameters including size, area, volume, and cardiac beating, offering a comprehensive live characterization and classification of the organoids. These findings provide profound insights into the differentiation and developmental processes of organoids, positioning quantitative OCT imaging as a potentially transformative tool for future organoid research.<p></p>

show abstract

Deep Dual Attention Network for Precise Diagnosis of COVID-19 From Chest CT Images

Cited by 26 publications

References 49 publications

Deep learning based characterization of human organoids using optical coherence tomography

Deep learning based characterization of human organoids using optical coherence tomography

DDRA-Net: Dual-Channel Deep Residual Attention UPerNet for Breast Lesions Segmentation in Ultrasound Images

Deep learning based characterization of human organoids using optical coherence tomography

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