Query：
学者姓名：李兰兰
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
Accurate and fast histological diagnosis of cancers is crucial for successful treatment. The deep learning-based approaches have assisted pathologists in efficient cancer diagnosis. The remodeled microenvironment and field cancerization may enable the cancer-specific features in the image of non-cancer regions surrounding cancer, which may provide additional information not available in the cancer region to improve cancer diagnosis. Here, we proposed a deep learning framework with fine-tuning target proportion towards cancer surrounding tissues in histological images for gastric cancer diagnosis. Through employing six deep learning-based models targeting region-of-interest (ROI) with different proportions of no-cancer and cancer regions, we uncovered the diagnostic value of non-cancer ROI, and the model performance for cancer diagnosis depended on the proportion. Then, we constructed a model based on MobileNetV2 with the optimized weights targeting non-cancer and cancer ROI to diagnose gastric cancer (DeepNCCNet). In the external validation, the optimized DeepNCCNet demonstrated excellent generalization abilities with an accuracy of 93.96%. In conclusion, we discovered a non-cancer ROI weight-dependent model performance, indicating the diagnostic value of non-cancer regions with potential remodeled microenvironment and field cancerization, which provides a promising image resource for cancer diagnosis. The DeepNCCNet could be readily applied to clinical diagnosis for gastric cancer, which is useful for some clinical settings such as the absence or minimum amount of tumor tissues in the insufficient biopsy.
Keyword ：
Cancer-adjacent tissues Cancer-adjacent tissues Cancer diagnosis Cancer diagnosis Deep learning Deep learning Field cancerization Field cancerization Histological image Histological image
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Lanlan , Geng, Yi , Chen, Tao et al. Deep learning model targeting cancer surrounding tissues for accurate cancer diagnosis based on histopathological images [J]. | JOURNAL OF TRANSLATIONAL MEDICINE , 2025 , 23 (1) . |
| MLA | Li, Lanlan et al. "Deep learning model targeting cancer surrounding tissues for accurate cancer diagnosis based on histopathological images" . | JOURNAL OF TRANSLATIONAL MEDICINE 23 . 1 (2025) . |
| APA | Li, Lanlan , Geng, Yi , Chen, Tao , Lin, Kaixin , Xie, Chengjie , Qi, Jing et al. Deep learning model targeting cancer surrounding tissues for accurate cancer diagnosis based on histopathological images . | JOURNAL OF TRANSLATIONAL MEDICINE , 2025 , 23 (1) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Background A singular reliable modality for early distinguishing perianal fi stulizing Crohn's disease (PFCD) from cryptoglandular fi stula (CGF) is currently lacking. We aimed to develop and validate an MRI-based deep learning classifier to effectively discriminate between them. Methods The present study retrospectively enrolled 1054 patients with PFCD or CGF from three Chinese tertiary referral hospitals between January 1, 2015, and December 31, 2021. The patients were divided into four cohorts: training cohort (n = 800), validation cohort (n = 100), internal test cohort (n = 100) and external test cohort (n = 54). Two deep convolutional neural networks (DCNN), namely MobileNetV2 and ResNet50, were respectively trained using the transfer learning strategy on a dataset consisting of 44871 MR images. The performance of the DCNN models was compared to that of radiologists using various metrics, including receiver operating characteristic curve (ROC) analysis, accuracy, sensitivity, and specificity. Delong testing was employed for comparing the area under curves (AUCs). Univariate and multivariate analyses were conducted to explore potential factors associated with classifier performance. Findings A total of 532 PFCD and 522 CGF patients were included. Both pre-trained DCNN classifiers achieved encouraging performances in the internal test cohort (MobileNetV2 AUC: 0.962, 95% CI 0.903-0.990; ResNet50 AUC: 0.963, 95% CI 0.905-0.990), as well as external test cohort (MobileNetV2 AUC: 0.885, 95% CI 0.769-0.956; ResNet50 AUC: 0.874, 95% CI 0.756-0.949). They had greater AUCs than the radiologists (all p <= 0.001), while had comparable AUCs to each other (p = 0.83 and p = 0.60 in the two test cohorts). None of the potential characteristics had a significant impact on the performance of pre-trained MobileNetV2 classifier in etiologic diagnosis. Previous fi stula surgery influenced the performance of the pre-trained ResNet50 classifier in the internal test cohort (OR 0.157, 95% CI 0.025-0.997, p = 0.05). Interpretation The developed DCNN classifiers exhibited superior robustness in distinguishing PFCD from CGF compared to artificial visual assessment, showing their potential for assisting in early detection of PFCD. Our fi ndings highlight the promising generalized performance of MobileNetV2 over ResNet50, rendering it suitable for deployment on mobile terminals.
Keyword ：
Deep convolutional neural network Deep convolutional neural network Deep learning Deep learning Pelvic MRI Pelvic MRI Perianal fistulizing Crohn's disease Perianal fistulizing Crohn's disease
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, Heng , Li, Wenru , Chen, Tao et al. Development and validation of the MRI-based deep learning classifier for distinguishing perianal fi stulizing Crohn's disease from cryptoglandular fi stula: a multicenter cohort study [J]. | ECLINICALMEDICINE , 2024 , 78 . |
| MLA | Zhang, Heng et al. "Development and validation of the MRI-based deep learning classifier for distinguishing perianal fi stulizing Crohn's disease from cryptoglandular fi stula: a multicenter cohort study" . | ECLINICALMEDICINE 78 (2024) . |
| APA | Zhang, Heng , Li, Wenru , Chen, Tao , Deng, Ke , Yang, Bolin , Luo, Jingen et al. Development and validation of the MRI-based deep learning classifier for distinguishing perianal fi stulizing Crohn's disease from cryptoglandular fi stula: a multicenter cohort study . | ECLINICALMEDICINE , 2024 , 78 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 et al. "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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
