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学者姓名：冯金超
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摘要 ：
In medical imaging, efficient segmentation of colon polyps plays a pivotal role in minimally invasive solutions for colorectal cancer. This study introduces a novel approach employing two parallel encoder branches within a network for polyp segmentation. One branch of the encoder incorporates the dual convolution blocks that have the capability to maintain feature information over increased depths, and the other block embraces the single convolution block with the addition of the previous layer's feature, offering diversity in feature extraction within the encoder, combining them before transpose layers with a depth-wise concatenation operation. Our model demonstrated superior performance, surpassing several established deep-learning architectures on the Kvasir and CVC-ClinicDB datasets, achieved a Dice score of 0.919, a mIoU of 0.866 for the Kvasir dataset, and a Dice score of 0.931 and a mIoU of 0.891 for the CVC-ClinicDB. The visual and quantitative results highlight the efficacy of our model, potentially setting a new model in medical image segmentation.
关键词 ：
Colonoscopy Images Colonoscopy Images Polyps Segmentation Polyps Segmentation Parallel Feature Extraction Parallel Feature Extraction Parallel Encoder Network Parallel Encoder Network
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| GB/T 7714 | Manan, Malik Abdul , Feng Jinchao , Ahmed, Shahzad et al. DPE-Net: Dual-Parallel Encoder Based Network for Semantic Segmentation of Polyps [J]. | 2024 9TH INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING, ICSIP , 2024 : 790-794 . |
| MLA | Manan, Malik Abdul et al. "DPE-Net: Dual-Parallel Encoder Based Network for Semantic Segmentation of Polyps" . | 2024 9TH INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING, ICSIP (2024) : 790-794 . |
| APA | Manan, Malik Abdul , Feng Jinchao , Ahmed, Shahzad , Raheem, Abdul . DPE-Net: Dual-Parallel Encoder Based Network for Semantic Segmentation of Polyps . | 2024 9TH INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING, ICSIP , 2024 , 790-794 . |
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摘要 ：
本发明公开了一种基于深度学习的扩散相关光谱无创血压连续监测方法，具体包括：首先，基于扩散相关光谱技术获取被测试者手臂部位的光强自相关函数数据，利用传统非线性拟合方法计算出组织血流指数；然后，基于所提出的U‑net网络将拟合出的组织血流指数数据进行训练，建立从组织血流指数到血压之间的端到端网络模型；最后，将测试集数据送入训练好的网络模型中，实现血压的预测，从而得到连续血压波形。本发明直接建立了组织血流指数与血压间的端到端关系，为无创血压连续监测提供了新方法，克服了现有无创血压连续监测方法操作繁琐、因袖带充气而导致不适等不足，为人们了解血压的起伏变化提供了方便。
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| GB/T 7714 | 李哲 , 白江涛 , 姜敏楠 et al. 一种基于深度学习的扩散相关光谱无创血压连续监测方法 : CN202310317145.3[P]. | 2023-03-26 . |
| MLA | 李哲 et al. "一种基于深度学习的扩散相关光谱无创血压连续监测方法" : CN202310317145.3. | 2023-03-26 . |
| APA | 李哲 , 白江涛 , 姜敏楠 , 冯金超 , 贾克斌 . 一种基于深度学习的扩散相关光谱无创血压连续监测方法 : CN202310317145.3. | 2023-03-26 . |
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摘要 ：
本发明公开了基于图卷积神经网络的近红外光谱层析成像重建方法，本发明提出一种基于图卷积的深度学习网络框架，该深度学习网络框架对具有不规则结构的成像域建立图模型，并将图结构信息加入到带有注意力机制的图卷积神经网络中，以提取图节点上的光学特征参数的特征，将采集到的光学信号作为网络输入进行端到端的训练，同时恢复出含氧血红蛋白，脱氧血红蛋白和水三种发色团的浓度。实验结果表明，本发明能够实现NIRST图像的准确重建。
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| GB/T 7714 | 冯金超 , 苏琳轩 , 魏承朴 et al. 基于图卷积神经网络的近红外光谱层析成像重建方法 : CN202310513333.3[P]. | 2023-05-09 . |
| MLA | 冯金超 et al. "基于图卷积神经网络的近红外光谱层析成像重建方法" : CN202310513333.3. | 2023-05-09 . |
| APA | 冯金超 , 苏琳轩 , 魏承朴 , 贾克斌 , 李哲 , 孙中华 . 基于图卷积神经网络的近红外光谱层析成像重建方法 : CN202310513333.3. | 2023-05-09 . |
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摘要 ：
As an emerging imaging technique, Cherenkov-excited luminescence scanned to-mography (CELST) can recover a high-resolution 3D distribution of quantum emission fields within tissue using X-ray excitation for deep penetrance. However, its reconstruction is an ill-posed and under-conditioned inverse problem because of the diffuse optical emission signal. Deep learning based image reconstruction has shown very good potential for solving these types of problems, however they suffer from a lack of ground-truth image data to confirm when used with experimental data. To overcome this, a self-supervised network cascaded by a 3D reconstruction network and the forward model, termed Selfrec-Net, was proposed to perform CELST reconstruction. Under this framework, the boundary measurements are input to the network to reconstruct the distribution of the quantum field and the predicted measurements are subsequently obtained by feeding the reconstructed result to the forward model. The network was trained by minimizing the loss between the input measurements and the predicted measurements rather than the reconstructed distributions and the corresponding ground truths. Comparative experiments were carried out on both numerical simulations and physical phantoms. For singular luminescent targets, the results demonstrate the effectiveness and robustness of the proposed network, and comparable performance can be attained to a state-of-the-art deep supervised learning algorithm, where the accuracy of the emission yield and localization of the objects was far superior to iterative reconstruction methods. Reconstruction of multiple objects is still reasonable with high localization accuracy, although with limits to the emission yield accuracy as the distribution becomes more complex. Overall though the reconstruction of Selfrec-Net provides a self-supervised way to recover the location and emission yield of molecular distributions in murine model tissues.& COPY; 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
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| GB/T 7714 | Zhang, Wenqian , Hu, Ting , Li, Zhe et al. Selfrec-Net: self-supervised deep learning approach for the reconstruction of Cherenkov-excited luminescence scanned tomography [J]. | BIOMEDICAL OPTICS EXPRESS , 2023 , 14 (2) : 783-798 . |
| MLA | Zhang, Wenqian et al. "Selfrec-Net: self-supervised deep learning approach for the reconstruction of Cherenkov-excited luminescence scanned tomography" . | BIOMEDICAL OPTICS EXPRESS 14 . 2 (2023) : 783-798 . |
| APA | Zhang, Wenqian , Hu, Ting , Li, Zhe , Sun, Zhonghua , Jia, Kebin , Dou, Huijing et al. Selfrec-Net: self-supervised deep learning approach for the reconstruction of Cherenkov-excited luminescence scanned tomography . | BIOMEDICAL OPTICS EXPRESS , 2023 , 14 (2) , 783-798 . |
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摘要 ：
Significance: Tissue phantoms that mimic the optical and radiologic properties of human or animal tissue play an important role in the development, characterization, and evaluation of imaging systems. Phantoms that are easily produced and stable for longitudinal studies are highly desirable.Aim: A new type of long-lasting phantom was developed with commercially available materials and was assessed for fabrication ease, stability, and optical property control. Magnetic resonance imaging (MRI) and x-ray computed tomography (CT) contrast properties were also evaluated.Approach: A systematic investigation of relationships between concentrations of skin-like pigments and composite optical properties was conducted to realize optical property phantoms in the red and near-infrared (NIR) wavelength range that also offered contrast for CT and MRI.Results: Phantom fabrication time was <1 h and did not involve any heating or cooling processes. Changes in optical properties were <2% over a 12-month period. Phantom optical and spectral features were similar to human soft tissue over the red to NIR wavelength ranges. Pigments used in the study also had CT and MRI contrasts for multimodality imaging studies.Conclusions: The phantoms described here mimic optical properties of soft tissue and are suitable for multimodality imaging studies involving CT or MRI without adding secondary contrast agents.
关键词 ：
spectroscopy spectroscopy tissue phantom tissue phantom multimodality imaging multimodality imaging optical property optical property
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| GB/T 7714 | Zhao, Mengyang , Zhou, Mingwei , Cao, Xu et al. Stable tissue-mimicking phantoms for longitudinal multimodality imaging studies that incorporate optical, CT, and MRI contrast [J]. | JOURNAL OF BIOMEDICAL OPTICS , 2023 , 28 (4) . |
| MLA | Zhao, Mengyang et al. "Stable tissue-mimicking phantoms for longitudinal multimodality imaging studies that incorporate optical, CT, and MRI contrast" . | JOURNAL OF BIOMEDICAL OPTICS 28 . 4 (2023) . |
| APA | Zhao, Mengyang , Zhou, Mingwei , Cao, Xu , Feng, Jinchao , Pogue, Brian W. , Paulsen, Keith D. et al. Stable tissue-mimicking phantoms for longitudinal multimodality imaging studies that incorporate optical, CT, and MRI contrast . | JOURNAL OF BIOMEDICAL OPTICS , 2023 , 28 (4) . |
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摘要 ：
Generative adversarial networks (GAN), which are fueled by deep learning, are an efficient technique for image reconstruction using under-sampled MR data. In most cases, the performance of a particular model's reconstruction must be improved by using a substantial proportion of the training data. However, gathering tens of thousands of raw patient data for training the model in actual clinical applications is difficult because retaining k-space data is not customary in the clinical process. Therefore, it is imperative to increase the generalizability of a network that was created using a small number of samples as quickly as possible. This research explored two unique applications based on deep learning-based GAN and transfer learning. Seeing as MRI reconstruction procedures go for brain and knee imaging, the proposed method outperforms current techniques in terms of signal-to-noise ratio (PSNR) and structural similarity index (SSIM). As compared to the results of transfer learning for the brain and knee, using a smaller number of training cases produced superior results, with acceleration factor (AF) 2 (for brain PSNR (39.33); SSIM (0.97), for knee PSNR (35.48); SSIM (0.90)) and AF 4 (for brain PSNR (38.13); SSIM (0.95), for knee PSNR (33.95); SSIM (0.86)). The approach that has been described would make it easier to apply future models for MRI reconstruction without necessitating the acquisition of vast imaging datasets.
关键词 ：
MRI MRI image reconstruction image reconstruction deep learning deep learning transfer learning transfer learning GANs GANs
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| GB/T 7714 | Yaqub, Muhammad , Feng Jinchao , Ahmed, Shahzad et al. GAN-TL: Generative Adversarial Networks with Transfer Learning for MRI Reconstruction [J]. | APPLIED SCIENCES-BASEL , 2022 , 12 (17) . |
| MLA | Yaqub, Muhammad et al. "GAN-TL: Generative Adversarial Networks with Transfer Learning for MRI Reconstruction" . | APPLIED SCIENCES-BASEL 12 . 17 (2022) . |
| APA | Yaqub, Muhammad , Feng Jinchao , Ahmed, Shahzad , Arshid, Kaleem , Bilal, Muhammad Atif , Akhter, Muhammad Pervez et al. GAN-TL: Generative Adversarial Networks with Transfer Learning for MRI Reconstruction . | APPLIED SCIENCES-BASEL , 2022 , 12 (17) . |
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摘要 ：
本发明公开了一种基于粗细粒度的地基云图分类方法，属于大气科学与计算机视觉领域。如何在不添加人工辅助信息和额外的物体位置信息标注的情况下提取更精细、更显著的纹理和形状特征仍是一个亟待解决的技术问题。本发明包含以下步骤：构建了一种弱监督学习的粗细粒度预测网络来提取云图具有辨别性的纹理特征，通过网络训练建立了云图全局特征与局部特征之间的联系；结合注意力学习和局部定位方法实现对云图显著性局部特征的定位和细化；最后，将粗细粒度预测结果相融合实现对目标的定位和分类并输出所属类别。实验结果表明本方法在地基云分类方面比强监督方法取得了更好的进展，达到98.58％的准确率，为设备集成与实际应用提供了可能性。
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| GB/T 7714 | 冯金超 , 邢乐园 , 孙中华 et al. 一种基于粗细粒度的地基云图分类方法 : CN202211117962.6[P]. | 2022-09-14 . |
| MLA | 冯金超 et al. "一种基于粗细粒度的地基云图分类方法" : CN202211117962.6. | 2022-09-14 . |
| APA | 冯金超 , 邢乐园 , 孙中华 , 贾克斌 . 一种基于粗细粒度的地基云图分类方法 : CN202211117962.6. | 2022-09-14 . |
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摘要 ：
本实用新型专利特别涉及一种MRI兼容的插拔式组织血流检测探头，用于解决目前扩散相关光谱技术组织血流仪测量过程中探头固定的问题。具体包括：探测光纤、光源光纤和柔性探头座。探头柔韧性高，可实现与被测组织的紧密贴合；探测光纤、光源光纤可与柔性固定探头座之间以插拔方式实现安装，探测光纤和光源光纤垂直于柔性探头底面；不同光源和探测间距，可实现不同深度的组织血流检测；探头各组成部分材制均不涉及金属，核磁共振(MRI)兼容。本实用新型专利以插拔方式解决了组织血流测量过程中探测光纤与光源光纤的固定问题，满足扩散相关光谱技术组织血流测量的理论模型要求，提高了组织血流测量的准确性和稳定性。
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| GB/T 7714 | 李哲 , 姜敏楠 , 冯金超 et al. 一种MRI兼容的插拔式组织血流检测探头 : CN202220215504.5[P]. | 2022-01-26 . |
| MLA | 李哲 et al. "一种MRI兼容的插拔式组织血流检测探头" : CN202220215504.5. | 2022-01-26 . |
| APA | 李哲 , 姜敏楠 , 冯金超 , 贾克斌 . 一种MRI兼容的插拔式组织血流检测探头 : CN202220215504.5. | 2022-01-26 . |
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摘要 ：
本发明公开了一种基于FPGA的组织氧代谢检测装置及方法，可实现组织氧代谢的便携式无创检测。所述装置包括：光源模块、探测器模块、FPGA处理模块、测量探头和显示模块。其中，光源模块为近红外长相干激光器；探测器模块为雪崩式光电二极管；FPGA处理模块用于计算组织氧代谢参数；测量探头用于固定光源光纤与探测光纤；显示模块用于参数实时显示。所述方法利用多波长DCS技术以时分方式将光源照射到被测组织表面，通过FPGA处理模块直接完成探测器采集数据的处理，即由FPGA处理模块替代上位机完成数据处理的全过程，极大地降低了装置体积和成本，为临床组织氧代谢检测提供一种便捷式的无创检测装置与方法。
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| GB/T 7714 | 李哲 , 姜敏楠 , 冯金超 et al. 一种基于FPGA的组织氧代谢检测装置及方法 : CN202210093772.9[P]. | 2022-01-26 . |
| MLA | 李哲 et al. "一种基于FPGA的组织氧代谢检测装置及方法" : CN202210093772.9. | 2022-01-26 . |
| APA | 李哲 , 姜敏楠 , 冯金超 , 贾克斌 . 一种基于FPGA的组织氧代谢检测装置及方法 : CN202210093772.9. | 2022-01-26 . |
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摘要 ：
本发明公开了一种基于FPGA的组织血流检测装置及方法，可实现对组织血流的便携式无创检测。所述装置包括：光源模块、探测器模块、FPGA处理模块、测量探头和显示模块。其中，光源模块为近红外长相干激光器；探测器模块为单光子计数器；FPGA处理模块用于计算组织血流；测量探头用于固定光源光纤与探测光纤；显示模块用于组织血流数据的实时显示。所述方法利用DCS技术将近红外波段光源照射与被测组织表面，通过FPGA处理模块实现探测器采集数据的分析处理，由FPGA处理模块替代传统上位机完成组织血流数据计算的全过程，在降低装置体积和成本的同时，为临床组织血流检测提供一种便捷式的无创检测装置与方法。
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| GB/T 7714 | 李哲 , 姜敏楠 , 冯金超 et al. 一种基于FPGA的组织血流检测装置及方法 : CN202210095295.X[P]. | 2022-01-26 . |
| MLA | 李哲 et al. "一种基于FPGA的组织血流检测装置及方法" : CN202210095295.X. | 2022-01-26 . |
| APA | 李哲 , 姜敏楠 , 冯金超 , 贾克斌 . 一种基于FPGA的组织血流检测装置及方法 : CN202210095295.X. | 2022-01-26 . |
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