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学者姓名：冯金超
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Abstract ：
Diffuse optical tomography (DOT) is a promising non-invasive optical imaging technology that can provide functional information of biological tissues. Since the diffused light undergoes multiple scattering in biological tissues, and the boundary measurements are limited, the inverse problem of DOT is ill-posed and ill-conditioned. To overcome these limitations, inverse problems in DOT are often mitigated using regularization techniques, which use data fitting and regularization terms to suppress the effects of measurement noise and modeling errors. Tikhonov regularization, utilizing the L2 norm as its regularization term, often leads to images that are excessively smooth. In recent years, with the continuous development of deep learning algorithms, many researchers have used Model-based deep learning methods for reconstruction. However, the reconstruction of DOT is solved on mesh, arising from a finite element method for inverse problems, it is difficult to use it directly for convolutional network. Therefore, we propose a model-based graph convolutional network (Model-GCN). Overall, Model-GCN achieves better image reconstruction results compared to Tikhonov, with lower absolute bias error (ABE). Specifically, for total hemoglobin (HbT) and water, the average reduction in ABE is 68.3% and 77.3%, respectively. Additionally, the peak signal-to-noise (PSNR) values are on average increased by 6.4dB and 7.0dB.
Keyword ：
diffuse optical tomography diffuse optical tomography graph convolutional network graph convolutional network model-based model-based
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| GB/T 7714 | Wei, Chengpu , Li, Zhe , Hu, Ting et al. Model-based graph convolutional network for diffuse optical tomography [J]. | MULTIMODAL BIOMEDICAL IMAGING XIX , 2024 , 12834 . |
| MLA | Wei, Chengpu et al. "Model-based graph convolutional network for diffuse optical tomography" . | MULTIMODAL BIOMEDICAL IMAGING XIX 12834 (2024) . |
| APA | Wei, Chengpu , Li, Zhe , Hu, Ting , Sun, Zhonghua , Jia, Kebin , Feng, Jinchao . Model-based graph convolutional network for diffuse optical tomography . | MULTIMODAL BIOMEDICAL IMAGING XIX , 2024 , 12834 . |
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Abstract ：
Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded Convolution Network (MMCC-Net), aimed at addressing the limitations of existing models, such as inadequate spatial dependence representation and the absence of multi-level feature integration during the decoding stage by integrating multi-scale and multi-path cascaded convolutional techniques and enhances feature aggregation through dual attention modules, skip connections, and a feature enhancer. MMCCNet achieves superior performance in identifying polyp areas at the pixel level. The Proposed MMCC-Net was tested across six public datasets and compared against eight SOTA models to demonstrate its efficiency in polyp segmentation. The MMCC-Net's performance shows Dice scores with confidence interval ranging between 77.43 +/- 0.12, (77.08, 77.56) and 94.45 +/- 0.12, (94.19, 94.71) and Mean Intersection over Union (MIoU) scores with confidence interval ranging from 72.71 +/- 0.19, (72.20, 73.00) to 90.16 +/- 0.16, (89.69, 90.53) on the six databases. These results highlight the model's potential as a powerful tool for accurate and efficient polyp segmentation, contributing to early detection and prevention strategies in colorectal cancer.
Keyword ：
Feature aggregation Feature aggregation Semantic segmentation Semantic segmentation Colorectal polyp Colorectal polyp Attention modules Attention modules Cascaded convolution network Cascaded convolution network
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| GB/T 7714 | Manan, Malik Abdul , Feng, Jinchao , Yaqub, Muhammad et al. Multi-scale and multi-path cascaded convolutional network for semantic segmentation of colorectal polyps [J]. | ALEXANDRIA ENGINEERING JOURNAL , 2024 , 105 : 341-359 . |
| MLA | Manan, Malik Abdul et al. "Multi-scale and multi-path cascaded convolutional network for semantic segmentation of colorectal polyps" . | ALEXANDRIA ENGINEERING JOURNAL 105 (2024) : 341-359 . |
| APA | Manan, Malik Abdul , Feng, Jinchao , Yaqub, Muhammad , Ahmed, Shahzad , Imran, Syed Muhammad Ali , Chuhan, Imran Shabir et al. Multi-scale and multi-path cascaded convolutional network for semantic segmentation of colorectal polyps . | ALEXANDRIA ENGINEERING JOURNAL , 2024 , 105 , 341-359 . |
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Abstract ：
Brain tumors, which are uncontrolled growths of brain cells, pose a threat to people worldwide. However, accurately classifying brain tumors through computerized methods has been difficult due to differences in size, shape, and location of the tumors and limitations in the medical field. Improved precision is critical in detecting brain tumors, as small errors in human judgments can result in increased mortality rates. This paper proposes a new method for improving early detection and decision-making in brain tumor severity using learning methodologies. Clinical data -sets are used to obtain benchmark images of brain tumors, which undergo pre-processing, data aug-mentation with a Generative Adversarial Network, and classification with an Adaptive Layer Cascaded ResNet (ALCResNet) optimized with Improved Border Collie Optimization (IBCO). The abnormal images are then segmented using the DeepLabV3 model and fed into the ALCRes-Net for final classification into Meningioma, Glioma, or Pituitary. The IBCO algorithm-based ALCResNet model outperforms other heuristic classifiers for brain tumor classification and severity estimation, with improvements ranging from 1.3% to 4.4% over COA-ALCResNet, DHOA-ALCResNet, MVO-ALCResNet, and BCO-ALCResNet. The IBCO algorithm-based ALCResNet model also achieves higher accuracy than non-heuristic classifiers such as CNN, DNN, SVM, and ResNet, with improvements ranging from 2.4% to 3.6% for brain tumor classification and 0.9% to 3.8% for severity estimation. The proposed method offers an automated classification and grading system for brain tumors and improves the accuracy of brain tumor classification and severity estimation, promoting more precise decision-making regarding diagnosis and treatment.& COPY; 2023 The Authors. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
Keyword ：
Adaptive Layer Cascaded ResNet Adaptive Layer Cascaded ResNet Generative Adversarial Net-work Generative Adversarial Net-work Brain Tumor Grading Sys-tem Brain Tumor Grading Sys-tem DeepLabV3 DeepLabV3 Brain Tumor Classification System Brain Tumor Classification System Improved Border Collie Optimization Improved Border Collie Optimization
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| GB/T 7714 | Yaqub, Muhammad , Jinchao, Feng , Ahmed, Shahzad et al. DeepLabV3, IBCO-based ALCResNet: A fully automated classification, and grading system for brain tumor [J]. | ALEXANDRIA ENGINEERING JOURNAL , 2023 , 76 : 609-627 . |
| MLA | Yaqub, Muhammad et al. "DeepLabV3, IBCO-based ALCResNet: A fully automated classification, and grading system for brain tumor" . | ALEXANDRIA ENGINEERING JOURNAL 76 (2023) : 609-627 . |
| APA | Yaqub, Muhammad , Jinchao, Feng , Ahmed, Shahzad , Mehmood, Atif , Chuhan, Imran Shabir , Manan, Malik Abdul et al. DeepLabV3, IBCO-based ALCResNet: A fully automated classification, and grading system for brain tumor . | ALEXANDRIA ENGINEERING JOURNAL , 2023 , 76 , 609-627 . |
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Abstract ：
Exudates are a common sign of diabetic retinopathy, which is a disease that affects the blood vessels in the retina. Early detection of exudates is critical to avoiding vision problems through continuous screening and treatment. In traditional clinical practice, the involved lesions are manually detected using photographs of the fundus. However, this task is cumbersome and time-consuming and requires intense effort due to the small size of the lesion and the low contrast of the images. Thus, computer-assisted diagnosis of retinal disease based on the detection of red lesions has been actively explored recently. In this paper, we present a comparison of deep convolutional neural network (CNN) architectures and propose a residual CNN with residual skip connections to reduce the parameter for the semantic segmentation of exudates in retinal images. A suitable image augmentation technique is used to improve the performance of network architecture. The proposed network can robustly segment exudates with high accuracy, which makes it suitable for diabetic retinopathy screening. A comparative performance analysis of three benchmark databases: E-ophtha, DIARETDB1, and Hamilton Ophthalmology Institute's Macular Edema, is presented. The proposed method achieves a precision of 0.95, 0.92, 0.97, accuracy of 0.98, 0.98, 0.98, sensitivity of 0.97, 0.95, 0.95, specificity of 0.99, 0.99, 0.99, and area under the curve of 0.97, 0.94, and 0.96, respectively.
Keyword ：
data augmentation data augmentation semantic segmentation semantic segmentation residual network residual network diabetic retinopathy diabetic retinopathy retinal image retinal image convolution neural network convolution neural network exudates exudates
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| GB/T 7714 | Manan, Malik Abdul , Jinchao, Feng , Khan, Tariq M. M. et al. Semantic segmentation of retinal exudates using a residual encoder-decoder architecture in diabetic retinopathy [J]. | MICROSCOPY RESEARCH AND TECHNIQUE , 2023 , 86 (11) : 1443-1460 . |
| MLA | Manan, Malik Abdul et al. "Semantic segmentation of retinal exudates using a residual encoder-decoder architecture in diabetic retinopathy" . | MICROSCOPY RESEARCH AND TECHNIQUE 86 . 11 (2023) : 1443-1460 . |
| APA | Manan, Malik Abdul , Jinchao, Feng , Khan, Tariq M. M. , Yaqub, Muhammad , Ahmed, Shahzad , Chuhan, Imran Shabir . Semantic segmentation of retinal exudates using a residual encoder-decoder architecture in diabetic retinopathy . | MICROSCOPY RESEARCH AND TECHNIQUE , 2023 , 86 (11) , 1443-1460 . |
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Abstract ：
Diffuse optical tomography (DOT) is a promising non-invasive optical imaging technique that can provide functional information of biological tissues. Since diffuse light undergoes multiple scattering in biological tissues and boundary measurements are limited, DOT reconstruction is ill-posedness and ill-conditioned. To overcome these limitations, Tikhonov regularization is the most popular algorithm. Recently, deep learning based reconstruction methods have attracted increasing attention, and promising results have been reported. However, they lack generalization for unstructured physical model. Therefore, a model-base convolution neural network framework (Model-CNN) is developed. It composes of two layers: data consistency layer and depth layer, which increases the interpretability of the model. Its performance is evaluated with numerical simulations. Our results demonstrate that Model-CNN can get better reconstructed results than those obtained by Tikhonov Regularization in terms of ABE, MSE, and PSNR.
Keyword ：
Convolutional neural network Convolutional neural network diffuse optical tomography diffuse optical tomography model-engine model-engine
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| GB/T 7714 | Wei, Chengpu , Li, Zhe , Sun, Zhonghua et al. Diffusion equation engine deep learning for diffuse optical tomography [J]. | MULTIMODAL BIOMEDICAL IMAGING XVII , 2022 , 11952 . |
| MLA | Wei, Chengpu et al. "Diffusion equation engine deep learning for diffuse optical tomography" . | MULTIMODAL BIOMEDICAL IMAGING XVII 11952 (2022) . |
| APA | Wei, Chengpu , Li, Zhe , Sun, Zhonghua , Jia, Kebin , Feng, Jinchao . Diffusion equation engine deep learning for diffuse optical tomography . | MULTIMODAL BIOMEDICAL IMAGING XVII , 2022 , 11952 . |
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Abstract ：
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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Abstract ：
本实用新型专利特别涉及一种MRI兼容的贴附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；探测光纤和光源光纤平行于柔性探头底面，易于固定；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。本实用新型专利以贴附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兼容的贴附式组织血流检测探头 : CN202220217251.5[P]. | 2022-01-26 . |
| MLA | 李哲 et al. "一种MRI兼容的贴附式组织血流检测探头" : CN202220217251.5. | 2022-01-26 . |
| APA | 李哲 , 姜敏楠 , 冯金超 , 贾克斌 . 一种MRI兼容的贴附式组织血流检测探头 : CN202220217251.5. | 2022-01-26 . |
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Abstract ：
Cherenkov-excited luminescence scanned imaging (CELSI) is a new emerging imaging modality, which uses linear accelerator (LINAC) to induce Cherenkov radiation, and then secondary excite molecular probes to produce luminescence. The tomographic distribution of the molecular probes can be recovered by a reconstruction algorithm. However, the reconstruction images usually suffer from many artifacts. To improve the image quality for tomographic reconstruction, we propose a reconstruction method based on learned KSVD. Numerical simulation experiments reveal that the proposed algorithm can reduce the artifacts in the reconstructed image. The quantitative results show that the structured similarity (SSIM) is improved more than 8.8% compared to the existing algorithms. In addition, our results also demonstrate that the proposed algorithm has the best performance under different noise levels (0.5%, 1%, 2%, and 4%).
Keyword ：
artifacts removal artifacts removal image reconstruction image reconstruction Cherenkov-excited luminescence scanned imaging Cherenkov-excited luminescence scanned imaging learned KSVD learned KSVD
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| GB/T 7714 | Zhang, Hu , Li, Zhe , Sun, Zhonghua et al. Improved image quality for Cherenkov-Excited Luminescence Scanned Tomography based on learned KSVD [J]. | MOLECULAR-GUIDED SURGERY: MOLECULES, DEVICES, AND APPLICATIONS VIII , 2022 , 11943 . |
| MLA | Zhang, Hu et al. "Improved image quality for Cherenkov-Excited Luminescence Scanned Tomography based on learned KSVD" . | MOLECULAR-GUIDED SURGERY: MOLECULES, DEVICES, AND APPLICATIONS VIII 11943 (2022) . |
| APA | Zhang, Hu , Li, Zhe , Sun, Zhonghua , Geng, Mengfan , Jia, Kebin , Feng, Jinchao . Improved image quality for Cherenkov-Excited Luminescence Scanned Tomography based on learned KSVD . | MOLECULAR-GUIDED SURGERY: MOLECULES, DEVICES, AND APPLICATIONS VIII , 2022 , 11943 . |
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Abstract ：
Cherenkov-excited luminescence scanned tomography (CELST) is an emerging tomographic optical imaging modality. However, recovering spatial distribution of luminescent source from boundary measurements is a typically ill-posed problem. To improve the performance of CELST reconstruction, an end-to-end reconstruction algorithm is developed by combining dilated convolution and attention mechanism based on Unet (DA-Unet). Its performance is validated with numerical simulations. The results reveal that DA-Unet has superior reconstruction performance with high spatial resolution. It achieves image quality with PSNR of more than 35 dB and SSIM of larger than 0.95. Furthermore, the DA-Unet can reconstruct luminescent source even with less boundary measurements.
Keyword ：
attention mechanism attention mechanism Cherenkov-excited luminescence scanned tomography Cherenkov-excited luminescence scanned tomography dilated convolution dilated convolution U-Net U-Net
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| GB/T 7714 | Zhang, Wenqian , Li, Zhe , Sun, Zhonghua et al. Cherenkov-excited luminescence scanned tomography reconstruction based on Unet [J]. | MOLECULAR-GUIDED SURGERY: MOLECULES, DEVICES, AND APPLICATIONS VIII , 2022 , 11943 . |
| MLA | Zhang, Wenqian et al. "Cherenkov-excited luminescence scanned tomography reconstruction based on Unet" . | MOLECULAR-GUIDED SURGERY: MOLECULES, DEVICES, AND APPLICATIONS VIII 11943 (2022) . |
| APA | Zhang, Wenqian , Li, Zhe , Sun, Zhonghua , Jia, Kebin , Feng, Jinchao . Cherenkov-excited luminescence scanned tomography reconstruction based on Unet . | MOLECULAR-GUIDED SURGERY: MOLECULES, DEVICES, AND APPLICATIONS VIII , 2022 , 11943 . |
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Abstract ：
Diffuse Optical Tomography ( DOT) is a promising non-invasive and relatively low-cost biomedical image technology. The aim of DOT is to reconstruct optical properties of the tissue from boundary measurements. However, the DOT reconstruction is a severely ill-posed problem. To reduce the ill-posedness of DOT and to improve image quality, image-guided DOT has attracted more attention. In this paper, a reconstruction algorithm for DOT is proposed based on the convolutional neural network (CNN). It uses both optical measurements and magnetic resonance imaging (MRI) images as the input of the CNN, and directly reconstructs the distribution of absorption coefficient. The merits of the proposed algorithm are without segmenting MRI images and modeling light propagation. The performance of the proposed algorithm is evaluated using numerical simulation experiments. Our results reveal that the proposed method can achieve superior performance compared with conventional reconstruction algorithms and other deep learning methods. Our result shows that the average SSIM of reconstructed images is above 0.88, and the average PSNR is more than 35 dB.
Keyword ：
image-guided reconstruction image-guided reconstruction Diffuse optical tomography Diffuse optical tomography convolutional neural network convolutional neural network deep learning deep learning
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| GB/T 7714 | Zhang, Wanlong , Li, Zhe , Sun, Zhonghua et al. A novelty Convolutional Neural Network Based Direct Reconstruction for MRI Guided Diffuse Optical Tomography [J]. | MULTIMODAL BIOMEDICAL IMAGING XVII , 2022 , 11952 . |
| MLA | Zhang, Wanlong et al. "A novelty Convolutional Neural Network Based Direct Reconstruction for MRI Guided Diffuse Optical Tomography" . | MULTIMODAL BIOMEDICAL IMAGING XVII 11952 (2022) . |
| APA | Zhang, Wanlong , Li, Zhe , Sun, Zhonghua , Jia, Kebin , Feng, Jinchao . A novelty Convolutional Neural Network Based Direct Reconstruction for MRI Guided Diffuse Optical Tomography . | MULTIMODAL BIOMEDICAL IMAGING XVII , 2022 , 11952 . |
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