In steel plants, estimation of the production system characteristic is highly critical to adjust the system parameters for best efficiency. Although the system parameters may be tuned very well, due to the machine and...In steel plants, estimation of the production system characteristic is highly critical to adjust the system parameters for best efficiency. Although the system parameters may be tuned very well, due to the machine and human factors involved in the production line some deficiencies may occur in product. It is important to detect such problems as early as possible. Surface defects and dimensional deviations are the most important quality problems. In this study, it is aimed to develop an approach to measure the dimensions of metal profiles by obtaining images of them. This will be of use in detecting the deviations in dimensions. A platform was introduced to simulate the real-time environment and images were taken from the metal profile using 4 laser light sources. The shape of the material is generated by combining the images taken from different cameras. Real dimensions were obtained by using image processing and mathematical conversion operations on the images. The results obtained with small deviations from the real values showed that this method can be applied in a real-time production line.展开更多
A new method for reconstructing a 3-dimensional object from serial cross-sectionsis presented in this paper.The method is based on the principle of sampling and considersevery point in cross-sections as a sampling poi...A new method for reconstructing a 3-dimensional object from serial cross-sectionsis presented in this paper.The method is based on the principle of sampling and considersevery point in cross-sections as a sampling point and performs the interpolating of nonlinearfunction with these sampling points.Compared with other methods,this method has manyadvantages such as higher precision and fewer requested known sampling points.The result ofreconstruction with this method is an“entity”which involves the exterior shape and interiorconstruction information of the object simultaneously.展开更多
Objective To evaluate the value of MRI diffusion weighted imaging in localization of prostate cancer with whole-mount step section pathology. Methods We treated 36 patients using laparoscopic radical prostatectomy fro...Objective To evaluate the value of MRI diffusion weighted imaging in localization of prostate cancer with whole-mount step section pathology. Methods We treated 36 patients using laparoscopic radical prostatectomy from Oct. 2009 to Jun. 2010. Patients who did not have an MRL /DWI examination or a surgical history of pros-展开更多
目的:本研究旨在利用深度学习技术分析结直肠癌(CRC)病理切片图像,预测与结直肠癌相关的微生物丰度。方法:研究团队整合了TCGA数据库中的病理图像与微生物数据,开发了MDLR-Mean(Microbe Deep Learning Regression Prediction model Mean...目的:本研究旨在利用深度学习技术分析结直肠癌(CRC)病理切片图像,预测与结直肠癌相关的微生物丰度。方法:研究团队整合了TCGA数据库中的病理图像与微生物数据,开发了MDLR-Mean(Microbe Deep Learning Regression Prediction model Mean)模型。该模型融合了TOAD(Tumour Origin Assessment via Deep Learning)的肿瘤起源评估能力和MLP的深度学习特性,通过特征聚合技术提升预测精度,并采用MAE损失函数优化模型表现。结果:实验结果显示,MDLR-Mean模型在微生物丰度预测上表现卓越,在皮尔逊相关系数(PCC)、均方误差(MSE)和平均绝对误差(MAE)评估指标上均表现优异(P<0.05)。尤其是平均PCC相较于现有方法提升了36.5%,验证了模型的高效性和准确性。结论:本研究成功验证了MDLR-Mean模型在预测结直肠癌病理切片图像中微生物丰度方面的高准确性和可靠性,揭示深度学习将在未来结直肠癌诊治中发挥重要作用和助力精准医疗。展开更多
Background The incidence of colorectal cancer is increasing worldwide,and it currently ranks third among all cancers.Moreover,pathological diagnosis is becoming increasingly arduous.Artificial intelligence has demon-s...Background The incidence of colorectal cancer is increasing worldwide,and it currently ranks third among all cancers.Moreover,pathological diagnosis is becoming increasingly arduous.Artificial intelligence has demon-strated the ability to fully excavate image features and assist doctors in making decisions.Large panoramic patho-logical sections contain considerable amounts of pathological information.In this study,we used large panoramic pathological sections to establish a deep learning model to assist pathologists in identifying cancerous areas on whole-slide images of rectal cancer,as well as for T staging and prognostic analysis.Methods We collected 126 cases of primary rectal cancer from the Affiliated Hospital of Qingdao University West Coast Hospital District(internal dataset)and 42 cases from Shinan and Laoshan Hospital District(external dataset)that had tissue surgically removed from January to September 2019.After sectioning,staining,and scanning,a total of 2350 hematoxylin-eosin-stained whole-slide images were obtained.The patients in the internal dataset were randomly divided into a training cohort(n=88)and a test cohort(n=38)at a ratio of 7:3.We chose DeepLabV3+and ResNet50 as target models for our experiment.We used the Dice similarity coefficient,accuracy,sensitivity,specificity,receiver operating characteristic(ROC)curve,and area under the curve(AUC)to evaluate the performance of the artificial intelligence platform in the test set and validation set.Finally,we followed up patients and examined their prognosis and short-term survival to corroborate the value of T-staging investigations.Results In the test set,the accuracy of image segmentation was 95.8%,the Dice coefficient was 0.92,the accuracy of automatic T-staging recognition was 86%,and the ROC AUC value was 0.93.In the validation set,the accuracy of image segmentation was 95.3%,the Dice coefficient was 0.90,the accuracy of automatic classification was 85%,the ROC AUC value was 0.92,and the image analysis time was 0.2 s.There was a difference in survival in patients with local recurrence or distant metastasis as the outcome at follow-up.Univariate analysis showed that T stage,N stage,preoperative carcinoembryonic antigen(CEA)level,and tumor location were risk factors for postoperative recurrence or metastasis in patients with rectal cancer.When these factors were included in a multivariate analysis,only preoperative CEA level and N stage showed significant differences.Conclusion The deep convolutional neural networks we have establish can assist clinicians in making decisions of T-stage judgment and improve diagnostic efficiency.Using large panoramic pathological sections enables better judgment of the condition of tumors and accurate pathological diagnoses,which has certain clinical application value.展开更多
文摘In steel plants, estimation of the production system characteristic is highly critical to adjust the system parameters for best efficiency. Although the system parameters may be tuned very well, due to the machine and human factors involved in the production line some deficiencies may occur in product. It is important to detect such problems as early as possible. Surface defects and dimensional deviations are the most important quality problems. In this study, it is aimed to develop an approach to measure the dimensions of metal profiles by obtaining images of them. This will be of use in detecting the deviations in dimensions. A platform was introduced to simulate the real-time environment and images were taken from the metal profile using 4 laser light sources. The shape of the material is generated by combining the images taken from different cameras. Real dimensions were obtained by using image processing and mathematical conversion operations on the images. The results obtained with small deviations from the real values showed that this method can be applied in a real-time production line.
文摘A new method for reconstructing a 3-dimensional object from serial cross-sectionsis presented in this paper.The method is based on the principle of sampling and considersevery point in cross-sections as a sampling point and performs the interpolating of nonlinearfunction with these sampling points.Compared with other methods,this method has manyadvantages such as higher precision and fewer requested known sampling points.The result ofreconstruction with this method is an“entity”which involves the exterior shape and interiorconstruction information of the object simultaneously.
文摘Objective To evaluate the value of MRI diffusion weighted imaging in localization of prostate cancer with whole-mount step section pathology. Methods We treated 36 patients using laparoscopic radical prostatectomy from Oct. 2009 to Jun. 2010. Patients who did not have an MRL /DWI examination or a surgical history of pros-
文摘目的:本研究旨在利用深度学习技术分析结直肠癌(CRC)病理切片图像,预测与结直肠癌相关的微生物丰度。方法:研究团队整合了TCGA数据库中的病理图像与微生物数据,开发了MDLR-Mean(Microbe Deep Learning Regression Prediction model Mean)模型。该模型融合了TOAD(Tumour Origin Assessment via Deep Learning)的肿瘤起源评估能力和MLP的深度学习特性,通过特征聚合技术提升预测精度,并采用MAE损失函数优化模型表现。结果:实验结果显示,MDLR-Mean模型在微生物丰度预测上表现卓越,在皮尔逊相关系数(PCC)、均方误差(MSE)和平均绝对误差(MAE)评估指标上均表现优异(P<0.05)。尤其是平均PCC相较于现有方法提升了36.5%,验证了模型的高效性和准确性。结论:本研究成功验证了MDLR-Mean模型在预测结直肠癌病理切片图像中微生物丰度方面的高准确性和可靠性,揭示深度学习将在未来结直肠癌诊治中发挥重要作用和助力精准医疗。
文摘Background The incidence of colorectal cancer is increasing worldwide,and it currently ranks third among all cancers.Moreover,pathological diagnosis is becoming increasingly arduous.Artificial intelligence has demon-strated the ability to fully excavate image features and assist doctors in making decisions.Large panoramic patho-logical sections contain considerable amounts of pathological information.In this study,we used large panoramic pathological sections to establish a deep learning model to assist pathologists in identifying cancerous areas on whole-slide images of rectal cancer,as well as for T staging and prognostic analysis.Methods We collected 126 cases of primary rectal cancer from the Affiliated Hospital of Qingdao University West Coast Hospital District(internal dataset)and 42 cases from Shinan and Laoshan Hospital District(external dataset)that had tissue surgically removed from January to September 2019.After sectioning,staining,and scanning,a total of 2350 hematoxylin-eosin-stained whole-slide images were obtained.The patients in the internal dataset were randomly divided into a training cohort(n=88)and a test cohort(n=38)at a ratio of 7:3.We chose DeepLabV3+and ResNet50 as target models for our experiment.We used the Dice similarity coefficient,accuracy,sensitivity,specificity,receiver operating characteristic(ROC)curve,and area under the curve(AUC)to evaluate the performance of the artificial intelligence platform in the test set and validation set.Finally,we followed up patients and examined their prognosis and short-term survival to corroborate the value of T-staging investigations.Results In the test set,the accuracy of image segmentation was 95.8%,the Dice coefficient was 0.92,the accuracy of automatic T-staging recognition was 86%,and the ROC AUC value was 0.93.In the validation set,the accuracy of image segmentation was 95.3%,the Dice coefficient was 0.90,the accuracy of automatic classification was 85%,the ROC AUC value was 0.92,and the image analysis time was 0.2 s.There was a difference in survival in patients with local recurrence or distant metastasis as the outcome at follow-up.Univariate analysis showed that T stage,N stage,preoperative carcinoembryonic antigen(CEA)level,and tumor location were risk factors for postoperative recurrence or metastasis in patients with rectal cancer.When these factors were included in a multivariate analysis,only preoperative CEA level and N stage showed significant differences.Conclusion The deep convolutional neural networks we have establish can assist clinicians in making decisions of T-stage judgment and improve diagnostic efficiency.Using large panoramic pathological sections enables better judgment of the condition of tumors and accurate pathological diagnoses,which has certain clinical application value.
