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学者姓名：李兰兰
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Abstract ：
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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| 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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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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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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目的 设计一种融合多模态图像深度学习模型CNN-ViT,诊断弥漫性大B细胞淋巴瘤(DLBCL)骨髓受累.资料与方法 回顾性收集2012年11月—2022年6月福建省立医院经病理证实的DLBCL 78例,其中无骨髓受累46例,有骨髓受累32例,所有患者在化疗前均行全身18F-FDG PET/CT检查、骨髓穿刺细胞涂片和(或)骨髓活检.选取骨盆区域PET及CT图像共9828张.将上述数据按7:1:2随机分为训练集6858张、验证集982张和测试集1988张.结合传统的卷积神经网络(CNN)和Vision-Transformer(ViT)模型设计CNN-ViT模型,分别提取PET和CT图像特征,预测骨髓受累情况.使用测试集的混淆矩阵和损失函数的变化、准确度、敏感度、特异度和F1_score评价模型的性能.结果 CNN-ViT模型诊断DLBCL骨髓受累的准确度、特异度、敏感度和F1_score分别为0.988、0.971、0.997、0.987.结论 CNN-ViT模型可以准确评估DLBCL骨髓受累情况.
Keyword ：
B细胞 B细胞 X线计算机 X线计算机 体层摄影术 体层摄影术 正电子发射断层显像术 正电子发射断层显像术 淋巴瘤 淋巴瘤 神经网络 神经网络 骨盆 骨盆 骨髓 骨髓
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| GB/T 7714 | 李兰兰 , 周颖 , 林禹 et al. 基于多模态图像构建CNN-ViT模型在弥漫性大B细胞淋巴瘤骨髓受累诊断中的应用 [J]. | 中国医学影像学杂志 , 2023 , 31 (4) : 390-394 . |
| MLA | 李兰兰 et al. "基于多模态图像构建CNN-ViT模型在弥漫性大B细胞淋巴瘤骨髓受累诊断中的应用" . | 中国医学影像学杂志 31 . 4 (2023) : 390-394 . |
| APA | 李兰兰 , 周颖 , 林禹 , 尤梦翔 , 林美福 , 陈文新 . 基于多模态图像构建CNN-ViT模型在弥漫性大B细胞淋巴瘤骨髓受累诊断中的应用 . | 中国医学影像学杂志 , 2023 , 31 (4) , 390-394 . |
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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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目的:初步探索基于深度卷积神经网络（DCNN）构建的克罗恩病（CD）肛瘘磁共振成像（MRI）诊断模型效能。方法:采用回顾性研究方法，随机纳入2014年1月至2019年12月中山大学附属第六医院收治的200例初诊CD肛瘘患者和200例初诊腺源性肛瘘患者，每组按8∶1∶1分配至训练集、验证集和测试集。收集所有患者肛管MRI图像，预处理增强图像质量。采用Pytorch深度学习框架和Windows10计算机操作系统，基于4种DCNN（MobileNetV2、VGG11、ResNet18和ResNet34）构建CD肛瘘和腺源性肛瘘的MRI鉴别诊断模型。每种模型根据是否结合迁移学习策略，分为迁移学习型（T）和非迁移学习型（U）。首先，输入训练集（CD肛瘘和腺源性肛瘘患者各160例，共78 321张MRI图像）图像数据，迭代训练至损失最小。然后，根据验证集（CD肛瘘和腺源性肛瘘患者各20例，共9697张MRI图像）的结果选择最佳的训练模型。最后，在测试集（CD肛瘘和腺源性肛瘘患者各20例，共9260张MRI图像）进行诊断效能评估。绘制每种预测模型的受试者操作特征（ROC）曲线并计算曲线下面积（AUC）。采用DeLong检验比较不同模型之间以及预测模型与不同年资放射科医生之间AUC的差异。结果:结合迁移学习策略的4种诊断模型的效能分别为MobileNetV2-T（AUC=0.943，95% CI：0.820 ~ 0.991），VGG11-T（AUC=0.935，95% CI：0.810 ~ 0.988），ResNet18-T（AUC=0.920，95% CI：0.789 ~ 0.988），ResNet34-T（AUC=0.929，95% CI：0.801 ~ 0.986）。结合迁移学习策略的4种模型AUC均高于低年资放射科医生（均 P<0.05），与高年资放射科医生的差异均无统计学意义（均 P>0.05）。 结论:采用基于DCNN的深度学习技术，结合迁移学习策略和高分辨率肛管MRI构建CD肛瘘的病因诊断模型具有可行性。
Keyword ：
人工智能 人工智能 克罗恩病 克罗恩病 深度卷积神经网络 深度卷积神经网络 深度学习 深度学习 磁共振成像 磁共振成像 肛瘘 肛瘘
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| GB/T 7714 | 李兰兰 , 邓珂 , 张恒 et al. 基于深度卷积神经网络的克罗恩病肛瘘磁共振成像诊断模型初探 [J]. | 中华炎性肠病杂志 , 2023 , 07 (2) : 144-150 . |
| MLA | 李兰兰 et al. "基于深度卷积神经网络的克罗恩病肛瘘磁共振成像诊断模型初探" . | 中华炎性肠病杂志 07 . 2 (2023) : 144-150 . |
| APA | 李兰兰 , 邓珂 , 张恒 , 任东林 , 李文儒 . 基于深度卷积神经网络的克罗恩病肛瘘磁共振成像诊断模型初探 . | 中华炎性肠病杂志 , 2023 , 07 (2) , 144-150 . |
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As a powerful machine learning technique, deep learning has been widely applied to lesion detection in medical image processing. This review summarizes the research progress of deep learning applications in lesion detection. Firstly, the characteristics of medical image data are introduced, and the datasets and evaluation metrics of lesion detection are summarized. Then, the main contents of deep learning, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), YOLO algorithm, and SAM, have demonstrated good performance in medical image processing. Meanwhile, the applications of lesion detection in different medical image modalities are discussed, and the advantages of deep learning in different lesion types are highlighted, such as high automation, good performance, and transferability. In addition, some challenges of deep learning in lesion detection are discussed, such as sample scarcity, interpretability, and reliability. Finally, the future development directions of deep learning in lesion detection are discussed, such as multimodal fusion, transfer learning, and labeled data. This review provides a comprehensive overview of the research progress of deep learning in the field of lesion detection, which offers guidance and reference for related research and applications. © 2023 IEEE.
Keyword ：
Convolutional neural networks Convolutional neural networks Generative adversarial networks Generative adversarial networks Learning systems Learning systems Medical imaging Medical imaging Recurrent neural networks Recurrent neural networks Transfer learning Transfer learning
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| GB/T 7714 | Chen, Tao , Geng, Yi , Li, Lanlan et al. A Review of Deep Learning Applications in Lesion Detection Research [C] . 2023 : 181-188 . |
| MLA | Chen, Tao et al. "A Review of Deep Learning Applications in Lesion Detection Research" . (2023) : 181-188 . |
| APA | Chen, Tao , Geng, Yi , Li, Lanlan , Wei, Hongan . A Review of Deep Learning Applications in Lesion Detection Research . (2023) : 181-188 . |
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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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