Since the discovery of therapeutic insulin in 1922 and the development of the non-obese diabetic spontaneous mouse model in 1980,the establishment of Network for Pancreatic Organ Donor with Diabetes(n POD) in 2007 is ...Since the discovery of therapeutic insulin in 1922 and the development of the non-obese diabetic spontaneous mouse model in 1980,the establishment of Network for Pancreatic Organ Donor with Diabetes(n POD) in 2007 is arguably the most important milestone step in advancing type 1 diabetes(T1D) research. In this perspective,we briefly describe how n POD is transforming T1 D research via procuring and coordinating analysis of disease pathogenesis directly in human organs donated by deceased diabetic and control subjects. The successful precedent set up by n POD is likely to spread far beyond the confines of research in T1 D to revolutionize biomedical research of other disease using high quality procured human cells and tissues.展开更多
Background Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications,particularly in visual recognition tasks such as image and video analyses.There is a growing...Background Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications,particularly in visual recognition tasks such as image and video analyses.There is a growing interest in applying this technology to diverse applications in medical image analysis.Automated three dimensional Breast Ultrasound is a vital tool for detecting breast cancer,and computer-assisted diagnosis software,developed based on deep learning,can effectively assist radiologists in diagnosis.However,the network model is prone to overfitting during training,owing to challenges such as insufficient training data.This study attempts to solve the problem caused by small datasets and improve model detection performance.Methods We propose a breast cancer detection framework based on deep learning(a transfer learning method based on cross-organ cancer detection)and a contrastive learning method based on breast imaging reporting and data systems(BI-RADS).Results When using cross organ transfer learning and BIRADS based contrastive learning,the average sensitivity of the model increased by a maximum of 16.05%.Conclusion Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced,and contrastive learning method based on BI-RADS can improve the detection performance of the model.展开更多
基金Supported by The United States National Institutes of Health,No.1R01AI099027 and 5R01DK104662(to Hamad ARA)
文摘Since the discovery of therapeutic insulin in 1922 and the development of the non-obese diabetic spontaneous mouse model in 1980,the establishment of Network for Pancreatic Organ Donor with Diabetes(n POD) in 2007 is arguably the most important milestone step in advancing type 1 diabetes(T1D) research. In this perspective,we briefly describe how n POD is transforming T1 D research via procuring and coordinating analysis of disease pathogenesis directly in human organs donated by deceased diabetic and control subjects. The successful precedent set up by n POD is likely to spread far beyond the confines of research in T1 D to revolutionize biomedical research of other disease using high quality procured human cells and tissues.
基金Macao Polytechnic University Grant(RP/FCSD-01/2022RP/FCA-05/2022)Science and Technology Development Fund of Macao(0105/2022/A).
文摘Background Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications,particularly in visual recognition tasks such as image and video analyses.There is a growing interest in applying this technology to diverse applications in medical image analysis.Automated three dimensional Breast Ultrasound is a vital tool for detecting breast cancer,and computer-assisted diagnosis software,developed based on deep learning,can effectively assist radiologists in diagnosis.However,the network model is prone to overfitting during training,owing to challenges such as insufficient training data.This study attempts to solve the problem caused by small datasets and improve model detection performance.Methods We propose a breast cancer detection framework based on deep learning(a transfer learning method based on cross-organ cancer detection)and a contrastive learning method based on breast imaging reporting and data systems(BI-RADS).Results When using cross organ transfer learning and BIRADS based contrastive learning,the average sensitivity of the model increased by a maximum of 16.05%.Conclusion Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced,and contrastive learning method based on BI-RADS can improve the detection performance of the model.
