Objective We developed a universal lesion detector(ULDor)which showed good performance in in-lab experiments.The study aims to evaluate the performance and its ability to generalize in clinical setting via both extern...Objective We developed a universal lesion detector(ULDor)which showed good performance in in-lab experiments.The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation.Methods The ULDor system consists of a convolutional neural network(CNN)trained on around 80 K lesion annotations from about 12 K CT studies in the DeepLesion dataset and 5 other public organ-specific datasets.During the validation process,the test sets include two parts:the external validation dataset which was comprised of 164 sets of non-contrasted chest and upper abdomen CT scans from a comprehensive hospital,and the internal validation dataset which was comprised of 187 sets of low-dose helical CT scans from the National Lung Screening Trial(NLST).We ran the model on the two test sets to output lesion detection.Three board-certified radiologists read the CT scans and verified the detection results of ULDor.We used positive predictive value(PPV)and sensitivity to evaluate the performance of the model in detecting space-occupying lesions at all extra-pulmonary organs visualized on CT images,including liver,kidney,pancreas,adrenal,spleen,esophagus,thyroid,lymph nodes,body wall,thoracic spine,etc.Results In the external validation,the lesion-level PPV and sensitivity of the model were 57.9%and 67.0%,respectively.On average,the model detected 2.1 findings per set,and among them,0.9 were false positives.ULDor worked well for detecting liver lesions,with a PPV of 78.9%and a sensitivity of 92.7%,followed by kidney,with a PPV of 70.0%and a sensitivity of 58.3%.In internal validation with NLST test set,ULDor obtained a PPV of 75.3%and a sensitivity of 52.0%despite the relatively high noise level of soft tissue on images.Conclusions The performance tests of ULDor with the external real-world data have shown its high effectiveness in multiple-purposed detection for lesions in certain organs.With further optimisation and iterative upgrades,ULDor may be well suited for extensive application to external data.展开更多
Learning English requires an English environment, but it is impossible for us to communicate with the English speaking people face-to-face frequently, Computer network can he said to narrow the distance between people...Learning English requires an English environment, but it is impossible for us to communicate with the English speaking people face-to-face frequently, Computer network can he said to narrow the distance between people from the space, turn the world into a global village, and provide people in the world with more and more convenient opporttmities. The use of computer-aided English teaching can make up for the deficiencies of the traditional English teaching methods, which will greatly improve English teaching. In this paper, in view of the perspective of psycholinguistics to analyze common English pod cast, network language and foreign learning phenomenon.展开更多
The implementation of the Smart schools project was one of the seven flagships＇ initiatives of the Malaysian Government aims at optimalising ICT utilisation in schools in line with Malaysia＇s aim to position the cou...The implementation of the Smart schools project was one of the seven flagships＇ initiatives of the Malaysian Government aims at optimalising ICT utilisation in schools in line with Malaysia＇s aim to position the country as a globally competitive knowledge-based economy. However, research studies reported a lack of success of the project towards promoting effective ICT usage and developing teacher professionally. This situation became the impetus for the development of the e-CPDelT research project which intends to develop an online learning system based on action research and personal involvement for Smart school teachers. The model adopted is loosely based on the successful UK-based Improving the Quality of Education for All （IQEA） proiect （Ainscow et al., 1994） and the Communities of Practice （CoP）＇s approach （Wenger, 1998）. The data were obtained from the blog entries made by the 20 participating Smart schools teachers and triangulated with focus group interviews and mentor reflections. The preliminary findings revealed both internal and external problems. Drawing upon these findings, the eCPDelT learning system was designed and steps were undertaken to promote the new system. However, the response to this promotion was far from satisfactory. The paper concludes by discussing the reasons for such disappointing results and proposes a new plan of action that involves the introduction of the Critical Friends Group technique,展开更多
文摘Objective We developed a universal lesion detector(ULDor)which showed good performance in in-lab experiments.The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation.Methods The ULDor system consists of a convolutional neural network(CNN)trained on around 80 K lesion annotations from about 12 K CT studies in the DeepLesion dataset and 5 other public organ-specific datasets.During the validation process,the test sets include two parts:the external validation dataset which was comprised of 164 sets of non-contrasted chest and upper abdomen CT scans from a comprehensive hospital,and the internal validation dataset which was comprised of 187 sets of low-dose helical CT scans from the National Lung Screening Trial(NLST).We ran the model on the two test sets to output lesion detection.Three board-certified radiologists read the CT scans and verified the detection results of ULDor.We used positive predictive value(PPV)and sensitivity to evaluate the performance of the model in detecting space-occupying lesions at all extra-pulmonary organs visualized on CT images,including liver,kidney,pancreas,adrenal,spleen,esophagus,thyroid,lymph nodes,body wall,thoracic spine,etc.Results In the external validation,the lesion-level PPV and sensitivity of the model were 57.9%and 67.0%,respectively.On average,the model detected 2.1 findings per set,and among them,0.9 were false positives.ULDor worked well for detecting liver lesions,with a PPV of 78.9%and a sensitivity of 92.7%,followed by kidney,with a PPV of 70.0%and a sensitivity of 58.3%.In internal validation with NLST test set,ULDor obtained a PPV of 75.3%and a sensitivity of 52.0%despite the relatively high noise level of soft tissue on images.Conclusions The performance tests of ULDor with the external real-world data have shown its high effectiveness in multiple-purposed detection for lesions in certain organs.With further optimisation and iterative upgrades,ULDor may be well suited for extensive application to external data.
文摘Learning English requires an English environment, but it is impossible for us to communicate with the English speaking people face-to-face frequently, Computer network can he said to narrow the distance between people from the space, turn the world into a global village, and provide people in the world with more and more convenient opporttmities. The use of computer-aided English teaching can make up for the deficiencies of the traditional English teaching methods, which will greatly improve English teaching. In this paper, in view of the perspective of psycholinguistics to analyze common English pod cast, network language and foreign learning phenomenon.
基金a research project (Code number:UKM-GUP-TMK-08-03-310) funded by a research grant provided by the National University of Malaysia
文摘The implementation of the Smart schools project was one of the seven flagships＇ initiatives of the Malaysian Government aims at optimalising ICT utilisation in schools in line with Malaysia＇s aim to position the country as a globally competitive knowledge-based economy. However, research studies reported a lack of success of the project towards promoting effective ICT usage and developing teacher professionally. This situation became the impetus for the development of the e-CPDelT research project which intends to develop an online learning system based on action research and personal involvement for Smart school teachers. The model adopted is loosely based on the successful UK-based Improving the Quality of Education for All （IQEA） proiect （Ainscow et al., 1994） and the Communities of Practice （CoP）＇s approach （Wenger, 1998）. The data were obtained from the blog entries made by the 20 participating Smart schools teachers and triangulated with focus group interviews and mentor reflections. The preliminary findings revealed both internal and external problems. Drawing upon these findings, the eCPDelT learning system was designed and steps were undertaken to promote the new system. However, the response to this promotion was far from satisfactory. The paper concludes by discussing the reasons for such disappointing results and proposes a new plan of action that involves the introduction of the Critical Friends Group technique,
