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学者姓名：李兰兰
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结肠镜检查依赖于操作人员且漏检率较高,所以需要一种实时的息肉分割算法,来辅助医生的息肉检测工作.因此论文提出短期密集连接注意网络(Short-Term Dense Concatenate Attention Network,STDCANet).网络编码端的核心层是短期密集连接注意模块,此模块整合了传统卷积、STDC、残差思想和NAM的优势,以较小的计算复杂度保留了可伸缩的感受野和多尺度信息,在解码端引入了PD解码器,摈弃了部分底层特征用于模型的加速,聚合了高层特征实现了较好的分割结果.STDCANet在CVC-ClinicDB数据集上与经典的医学图像分割网络进行性能和模型复杂度的对比,在这两方面均优于对比网络,有临床实时分割的潜力.
Keyword ：
医学图像处理 医学图像处理 注意力机制 注意力机制 深度学习 深度学习 结肠镜图像 结肠镜图像
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| GB/T 7714 | 李兰兰 , 张孝辉 , 王大彪 . 基于短期密集连接注意网络的结肠息肉分割方法 [J]. | 计算机与数字工程 , 2024 , 52 (8) : 2469-2472,2497 . |
| MLA | 李兰兰 等. "基于短期密集连接注意网络的结肠息肉分割方法" . | 计算机与数字工程 52 . 8 (2024) : 2469-2472,2497 . |
| APA | 李兰兰 , 张孝辉 , 王大彪 . 基于短期密集连接注意网络的结肠息肉分割方法 . | 计算机与数字工程 , 2024 , 52 (8) , 2469-2472,2497 . |
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Gastric cancer is a serious malignant tumor. The gold standard for diagnosing gastric cancer is identifying cancer cells using pathological slides under microscopic examination. While many approaches have been proposed for gastric cancer segmentation, it is still difficult to train large-scale segmentation networks with scant gastroscopy data. Recently, Segmentation Anything Model (SAM) has received a lot of interest lately for its use in segmenting natural and medical images. However, due to high computational complexity and huge computational costs, the application of SAM in resource limited embedded medical devices is limited. In this paper, we proposed GC-SAM, a lightweight model for tumor segmentation. The prompt encoder and mask decoder have been fine-tuned to better face the challenge of segmenting pathological images of gastric cancer tissue. Evaluated on an internal dataset, the GC-SAM achieved state-of-the-art performance compared to classical image segmentation networks, with Dice coefficient of 0.8186. In addition, external validation has confirmed its superior generalization ability. This study demonstrates the great potential of adapting GC-SAM to pathological image segmentation tasks in gastric cancer tissue and provides the possibility for deep learning image segmentation to be transferred to embedded medical devices. © 2024 IEEE.
Keyword ：
External validation External validation Fine-tune Fine-tune Gastric cancer Gastric cancer Image segmentation Image segmentation Knowledge distillation Knowledge distillation SAM SAM
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| GB/T 7714 | Li, L. , Geng, Y. , Huang, L. et al. Segmentation of Gastric Cancer Pathological Slice Cancerous Region Based on Lightweight Improved SAM [未知]. |
| MLA | Li, L. et al. "Segmentation of Gastric Cancer Pathological Slice Cancerous Region Based on Lightweight Improved SAM" [未知]. |
| APA | Li, L. , Geng, Y. , Huang, L. , Li, J. , Niu, D. . Segmentation of Gastric Cancer Pathological Slice Cancerous Region Based on Lightweight Improved SAM [未知]. |
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为解决深度学习分割算法在病灶的细节分割上存在漏判且模型参数量较大不利于实际应用的问题,提出一种基于改进的CG-Net的深度轻量化分割神经网络.在编码块加入改进高效金字塔拆分注意力模块和深度可分离卷积,以学习丰富多尺度全局特征;采用残差思想将注意力模块与编码块结合,提出高效金字塔语境引导模块,帮助网络学习全局和局部特征信息.在中山大学附属第六医院提供的腹部MRI图像数据库的结直肠肿瘤病灶分割实验中,验证了改进模型算法在分割精度和模型轻量化方面的有效性.
Keyword ：
医学图像分割 医学图像分割 注意力机制 注意力机制 深度可分离卷积 深度可分离卷积 深度学习 深度学习 结直肠癌 结直肠癌 编码解码网络 编码解码网络 轻量级 轻量级
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| GB/T 7714 | 李兰兰 , 胡益煌 , 王大彪 et al. CG-Net改进的结直肠癌病灶分割算法 [J]. | 计算机工程与设计 , 2024 , 45 (1) : 299-306 . |
| MLA | 李兰兰 et al. "CG-Net改进的结直肠癌病灶分割算法" . | 计算机工程与设计 45 . 1 (2024) : 299-306 . |
| APA | 李兰兰 , 胡益煌 , 王大彪 , 徐斌 , 李娟 . CG-Net改进的结直肠癌病灶分割算法 . | 计算机工程与设计 , 2024 , 45 (1) , 299-306 . |
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研究的目的是建立一个深度学习模型，用于进行直肠癌患者新辅助放化疗后的病理完全反应的预测。回顾性分析了99例直肠癌患者的MR影像资料，并按照训练组(71例)和测试组(28例)进行划分构成数据集。通过U-Net定位分割出肿瘤大致区域，在预测阶段通过改变神经网络卷积层数和切片大小得到了9个基础预测模型，并且利用权重分配法对预测得分进行修正。在验证组9个模型中，切片大小为256*256时，包含4个卷积层的模型整体性能最好，3折交叉验证中平均准确率、特异性和敏感性分别达到了0.714、0.717和0.708。研究构建的模型可以作为辅助工具对结直肠癌晚期患者对新辅助治疗的病理反应进行预测，预测精度较好，可为临床治疗提供参考。
Keyword ：
新辅助放化疗 新辅助放化疗 病理完全反应预测 病理完全反应预测 直肠癌 直肠癌 磁共振图像 磁共振图像 神经网络 神经网络
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| GB/T 7714 | 李兰兰 , 徐斌 , 李娟 et al. 基于权重分配的直肠癌病理完全反应预测算法 [J]. | 计算机仿真 , 2024 , 41 (04) : 314-319 . |
| MLA | 李兰兰 et al. "基于权重分配的直肠癌病理完全反应预测算法" . | 计算机仿真 41 . 04 (2024) : 314-319 . |
| APA | 李兰兰 , 徐斌 , 李娟 , 王大彪 . 基于权重分配的直肠癌病理完全反应预测算法 . | 计算机仿真 , 2024 , 41 (04) , 314-319 . |
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Neurons can be abstractly represented as skeletons due to the filament nature of neurites. With the rapid development of imaging and image analysis techniques, an increasing amount of neuron skeleton data is being produced. In some scientific studies, it is necessary to dissect the axons and dendrites, which is typically done manually and is both tedious and time-consuming. To automate this process, we have developed a method that relies solely on neuronal skeletons using Geometric Deep Learning (GDL). We demonstrate the effectiveness of this method using pyramidal neurons in mammalian brains, and the results are promising for its application in neuroscience studies.
Keyword ：
geometric deep learning geometric deep learning neuron skeleton neuron skeleton point cloud point cloud Pyramidal neuron Pyramidal neuron semantic segmentation semantic segmentation
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| GB/T 7714 | Li, Lanlan , Qi, Jing , Geng, Yi et al. Semantic segmentation of pyramidal neuron skeletons using geometric deep learning [J]. | JOURNAL OF INNOVATIVE OPTICAL HEALTH SCIENCES , 2023 , 16 (06) . |
| MLA | Li, Lanlan et al. "Semantic segmentation of pyramidal neuron skeletons using geometric deep learning" . | JOURNAL OF INNOVATIVE OPTICAL HEALTH SCIENCES 16 . 06 (2023) . |
| APA | Li, Lanlan , Qi, Jing , Geng, Yi , Wu, Jingpeng . Semantic segmentation of pyramidal neuron skeletons using geometric deep learning . | JOURNAL OF INNOVATIVE OPTICAL HEALTH SCIENCES , 2023 , 16 (06) . |
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Background: Accurate outcome prediction prior to treatment can facilitate trial design and clinical deci-sion making to achieve better treatment outcome.Method: We developed the DeepTOP tool with deep learning approach for region-of-interest segmenta-tion and clinical outcome prediction using magnetic resonance imaging (MRI). DeepTOP was constructed with an automatic pipeline from tumor segmentation to outcome prediction. In DeepTOP, the segmenta-tion model used U-Net with a codec structure, and the prediction model was built with a three-layer con-volutional neural network. In addition, the weight distribution algorithm was developed and applied in the prediction model to optimize the performance of DeepTOP.Results: A total of 1889 MRI slices from 99 patients in the phase III multicenter randomized clinical trial (NCT01211210) on neoadjuvant treatment for rectal cancer was used to train and validate DeepTOP. We systematically optimized and validated DeepTOP with multiple devised pipelines in the clinical trial, demonstrating a better performance than other competitive algorithms in accurate tumor segmentation (Dice coefficient: 0.79; IoU: 0.75; slice-specific sensitivity: 0.98) and predicting pathological complete response to chemo/radiotherapy (accuracy: 0.789; specificity: 0.725; and sensitivity: 0.812). DeepTOP is a deep learning tool that could avoid manual labeling and feature extraction and realize automatic tumor segmentation and treatment outcome prediction by using the original MRI images.Conclusion: DeepTOP is open to provide a tractable framework for the development of other segmenta-tion and predicting tools in clinical settings. DeepTOP-based tumor assessment can provide a reference for clinical decision making and facilitate imaging marker-driven trial design.(c) 2023 Elsevier B.V. All rights reserved. Radiotherapy and Oncology 183 (2023) 109550
Keyword ：
Cancer treatment Cancer treatment Magnetic resonance image Magnetic resonance image Neural network Neural network Treatment response Treatment response
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| GB/T 7714 | Li, Lanlan , Xu, Bin , Zhuang, Zhuokai et al. Accurate tumor segmentation and treatment outcome prediction with DeepTOP [J]. | RADIOTHERAPY AND ONCOLOGY , 2023 , 183 . |
| MLA | Li, Lanlan et al. "Accurate tumor segmentation and treatment outcome prediction with DeepTOP" . | RADIOTHERAPY AND ONCOLOGY 183 (2023) . |
| APA | Li, Lanlan , Xu, Bin , Zhuang, Zhuokai , Li, Juan , Hu, Yihuang , Yang, Hui et al. Accurate tumor segmentation and treatment outcome prediction with DeepTOP . | RADIOTHERAPY AND ONCOLOGY , 2023 , 183 . |
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The heat transfer of supercritical R134a in a horizontal internally ribbed tube was predicted by using a back propagation artificial neural network (ANN). The network was trained based on 4440 experimental data points. The effects of the network input parameters, data division method, training function, transfer function, number of hidden layers, and number of neurons on the prediction results were analyzed in detail, and a new empirical formula for determining the optimal number of neurons was proposed. The prediction results by the network were then compared with those of four traditional classical correlations. The results revealed that the mean absolute errors of the ANN for predicting Nutop and Nubottom were only 35.28% and 33.03%, respectively, of those of the traditional model. Furthermore, 99.02% of Nu could be predicted with deviations smaller than 30% by the ANN, whereas only 88.7% could be predicted by traditional correlations, indicating that the ANN has a higher prediction accuracy. The present study provides a useful reference for the application and optimization of ANNs for heat transfer prediction and the design of supercritical fluid heaters.
Keyword ：
Artificial neural networks Artificial neural networks Heat transfer performance prediction Heat transfer performance prediction R134a R134a Supercritical Supercritical
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| GB/T 7714 | Wang, Dabiao , Guo, Shizhang , Zhao, Yuan et al. Use of an artificial neural network to predict the heat transfer of supercritical R134a in a horizontal internally ribbed tube [J]. | APPLIED THERMAL ENGINEERING , 2023 , 228 . |
| MLA | Wang, Dabiao et al. "Use of an artificial neural network to predict the heat transfer of supercritical R134a in a horizontal internally ribbed tube" . | APPLIED THERMAL ENGINEERING 228 (2023) . |
| APA | Wang, Dabiao , Guo, Shizhang , Zhao, Yuan , Li, Sichong , Li, Lanlan . Use of an artificial neural network to predict the heat transfer of supercritical R134a in a horizontal internally ribbed tube . | APPLIED THERMAL ENGINEERING , 2023 , 228 . |
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A novel technique is proposed for simultaneous monitoring of optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD). The scheme is based on the cross-phase modulation effect in a highly nonlinear fiber, which induces a change in the optical tones according to the impairments. The power ratio of the optical tones at two separate frequencies is utilized for monitoring. Improvements in the maximum measurable OSNR, CD and dynamic range can be obtained using only the clock tone for the non-return-to-zero, return-to-zero with a 50% duty cycle differential quadrature phase-shift keying, differential phase-shift keying and on-off keying signals at 80 Gb/s. For the return to zero differential quadrature phase shift keying (RZ-DQPSK) system, the OSNR of 4–40 dB can be monitored with a 36.73 dB dynamic range, and a chromatic dispersion of 0–117 ps/nm can be monitored with a 48.68 dB dynamic range. A correlation is built to describe the relationship of the power ratio with the OSNR and chromatic dispersion which has a root-mean-squared error of 1.41. The delay group dispersion insensitivity, the impact of the input signal power, and the filter selection are investigated. The design enables accurate monitoring, simple operation and may be used in a wide range. © 2023 Elsevier GmbH
Keyword ：
Chromatic dispersion Chromatic dispersion Cross-phase modulation Cross-phase modulation Optical performance monitoring Optical performance monitoring Optical signal-to-noise ratio Optical signal-to-noise ratio Simultaneous monitoring Simultaneous monitoring
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| GB/T 7714 | Li, L. , Qi, J. , Xie, C. . Simultaneous OSNR and chromatic dispersion monitoring based on optical tones power ratio [J]. | Optik , 2023 , 287 . |
| MLA | Li, L. et al. "Simultaneous OSNR and chromatic dispersion monitoring based on optical tones power ratio" . | Optik 287 (2023) . |
| APA | Li, L. , Qi, J. , Xie, C. . Simultaneous OSNR and chromatic dispersion monitoring based on optical tones power ratio . | Optik , 2023 , 287 . |
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Neoadjuvant systemic treatment before surgery is a prevalent regimen in the patients with advanced-stage or high-risk tumor, which has shaped the treatment strategies and cancer survival in the past decades. However, some patients present with poor response to the neoadjuvant treatment. Therefore, it is of great significance to develop tools to help distinguish the patients that could achieve pathological complete response before surgery to avoid inappropriate treat-ment. Here, this study demonstrated a multi-task deep learning tool called DeepInteg. In the DeepInteg framework, the segmentation module was constructed based on the CE-Net with a context extractor to achieve end-to-end delineation of region of interest (ROI) from radiological images, then the features of segmented Magnetic Resonance Imaging (MRI) and Computed To-mography (CT) images of each case were fused and input to the classification module based on a convolution neural network for treatment outcome prediction. The dataset with 1700 MRI and CT slices collected from the prospectively randomized clinical trial (NCT01211210) on systemic treatment for rectal cancer was used to develop and systematically optimize DeepInteg. As a result, DeepInteg achieved automatic segmentation of tumoral ROI with Dices of 0.766 and 0.719 and mIoUs of 0.788 and 0.756 in CT and MRI images, respectively. In addition, DeepInteg ach-ieved AUC of 0.833, accuracy of 0.826 and specificity of 0.856 in the prediction for pathological complete response after treatment, which showed better performance compared with the model based on CT or MRI alone. This study provide a robust framework to develop disease-specific tools for automatic delineation of ROI and clinical outcome prediction. The well-trained Deep-Integ could be readily applied in clinic to predict pathological complete response after neo-adjuvant therapy in rectal cancer patients.
Keyword ：
CT CT Deep learning Deep learning MRI MRI Neoadjuvant therapy Neoadjuvant therapy Rectal cancer Rectal cancer
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| GB/T 7714 | Hu, Yihuang , Li, Juan , Zhuang, Zhuokai et al. Automatic treatment outcome prediction with DeepInteg based on multimodal radiological images in rectal cancer [J]. | HELIYON , 2023 , 9 (2) . |
| MLA | Hu, Yihuang et al. "Automatic treatment outcome prediction with DeepInteg based on multimodal radiological images in rectal cancer" . | HELIYON 9 . 2 (2023) . |
| APA | Hu, Yihuang , Li, Juan , Zhuang, Zhuokai , Xu, Bin , Wang, Dabiao , Yu, Huichuan et al. Automatic treatment outcome prediction with DeepInteg based on multimodal radiological images in rectal cancer . | HELIYON , 2023 , 9 (2) . |
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| GB/T 7714 | Zhang, H. , Li, L. , Deng, K. et al. Developing Preliminary MRI-based Classifier for Perianal Fistulizing Crohn's Disease by Using Deep Convolutional Neural Networks [J]. | JOURNAL OF CROHNS & COLITIS , 2023 , 17 : 474-475 . |
| MLA | Zhang, H. et al. "Developing Preliminary MRI-based Classifier for Perianal Fistulizing Crohn's Disease by Using Deep Convolutional Neural Networks" . | JOURNAL OF CROHNS & COLITIS 17 (2023) : 474-475 . |
| APA | Zhang, H. , Li, L. , Deng, K. , Li, W. , Ren, D. . Developing Preliminary MRI-based Classifier for Perianal Fistulizing Crohn's Disease by Using Deep Convolutional Neural Networks . | JOURNAL OF CROHNS & COLITIS , 2023 , 17 , 474-475 . |
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