Patients with type 2 diabetes mellitus(T2 DM) often have cognitive impairment and structural brain abnormalities.The magnetic resonance imaging(MRI)-based brain atrophy and lesion index can be used to evaluate common ...Patients with type 2 diabetes mellitus(T2 DM) often have cognitive impairment and structural brain abnormalities.The magnetic resonance imaging(MRI)-based brain atrophy and lesion index can be used to evaluate common brain changes and their correlation with cognitive function,and can therefore also be used to reflect whole-brain structural changes related to T2 DM.A total of 136 participants(64 men and 72 women,aged 55–86 years) were recruited for our study between January 2014 and December 2016.All participants underwent MRI and Mini-Mental State Examination assessment(including 42 healthy control,38 T2 DM without cognitive impairment,26 with cognitive impairment but without T2 DM,and 30 T2 DM with cognitive impairment participants).The total and sub-category brain atrophy and lesion index scores in patients with T2 DM with cognitive impairment were higher than those in healthy controls.Differences in the brain atrophy and lesion index of gray matter lesions and subcortical dilated perivascular spaces were found between non-T2 DM patients with cognitive impairment and patients with T2 DM and cognitive impairment.After adjusting for age,the brain atrophy and lesion index retained its capacity to identify patients with T2 DM with cognitive impairment.These findings suggest that the brain atrophy and lesion index,based on T1-weighted and T2-weighted imaging,is of clinical value for identifying patients with T2 DM and cognitive impairment.Gray matter lesions and subcortical dilated perivascular spaces may be potential diagnostic markers of T2 DM that is complicated by cognitive impairment.This study was approved by the Medical Ethics Committee of University of South China(approval No.USC20131109003) on November 9,2013,and was retrospectively registered with the Chinese Clinical Trial Registry(registration No.Chi CTR1900024150) on June 27,2019.展开更多
Skin lesions are in a category of disease that is both common in humans and a major cause of death.The classification accuracy of skin lesions is a crucial determinant of the success rate of curing lethal diseases.Dee...Skin lesions are in a category of disease that is both common in humans and a major cause of death.The classification accuracy of skin lesions is a crucial determinant of the success rate of curing lethal diseases.Deep Convolutional Neural Networks(CNNs)are now the most prevalent computer algorithms for the purpose of disease classification.As with all algorithms,CNNs are sensitive to noise from imaging devices,which often contaminates the quality of the images that are fed into them.In this paper,a deep CNN(Inception-v3)is used to study the effect of image noise on the classification of skin lesions.Gaussian noise,impulse noise,and noise made up of a compound of the two are added to an image dataset,namely the Dermofit Image Library from the University of Edinburgh.Evaluations,based on t-distributed Stochastic Neighbor Embedding(t-SNE)visualization,Receiver Operating Characteristic(ROC)analysis,and saliency maps,demonstrate the reliability of the Inception-v3 deep CNN in classifying noisy skin lesion images.展开更多
基金supported by the National Natural Science Foundation of China,No.81271538 (to SNP)345 Talent Project and the Natural Science Foundation of Liaoning Province of China,No.2019-ZD-0794 (to SNP)+1 种基金the Natural Science Foundation of Hunan Province of China,Nos.2017JJ2225 (to JCL),2018JJ2357 (to GHL)Hunan Provincial Science and Technology Innovation Program of China,No.2017SK50203 (to HZ)。
文摘Patients with type 2 diabetes mellitus(T2 DM) often have cognitive impairment and structural brain abnormalities.The magnetic resonance imaging(MRI)-based brain atrophy and lesion index can be used to evaluate common brain changes and their correlation with cognitive function,and can therefore also be used to reflect whole-brain structural changes related to T2 DM.A total of 136 participants(64 men and 72 women,aged 55–86 years) were recruited for our study between January 2014 and December 2016.All participants underwent MRI and Mini-Mental State Examination assessment(including 42 healthy control,38 T2 DM without cognitive impairment,26 with cognitive impairment but without T2 DM,and 30 T2 DM with cognitive impairment participants).The total and sub-category brain atrophy and lesion index scores in patients with T2 DM with cognitive impairment were higher than those in healthy controls.Differences in the brain atrophy and lesion index of gray matter lesions and subcortical dilated perivascular spaces were found between non-T2 DM patients with cognitive impairment and patients with T2 DM and cognitive impairment.After adjusting for age,the brain atrophy and lesion index retained its capacity to identify patients with T2 DM with cognitive impairment.These findings suggest that the brain atrophy and lesion index,based on T1-weighted and T2-weighted imaging,is of clinical value for identifying patients with T2 DM and cognitive impairment.Gray matter lesions and subcortical dilated perivascular spaces may be potential diagnostic markers of T2 DM that is complicated by cognitive impairment.This study was approved by the Medical Ethics Committee of University of South China(approval No.USC20131109003) on November 9,2013,and was retrospectively registered with the Chinese Clinical Trial Registry(registration No.Chi CTR1900024150) on June 27,2019.
文摘Skin lesions are in a category of disease that is both common in humans and a major cause of death.The classification accuracy of skin lesions is a crucial determinant of the success rate of curing lethal diseases.Deep Convolutional Neural Networks(CNNs)are now the most prevalent computer algorithms for the purpose of disease classification.As with all algorithms,CNNs are sensitive to noise from imaging devices,which often contaminates the quality of the images that are fed into them.In this paper,a deep CNN(Inception-v3)is used to study the effect of image noise on the classification of skin lesions.Gaussian noise,impulse noise,and noise made up of a compound of the two are added to an image dataset,namely the Dermofit Image Library from the University of Edinburgh.Evaluations,based on t-distributed Stochastic Neighbor Embedding(t-SNE)visualization,Receiver Operating Characteristic(ROC)analysis,and saliency maps,demonstrate the reliability of the Inception-v3 deep CNN in classifying noisy skin lesion images.
