Dermoscopic Image Classification with Neural Style Transfer

Li, Jack Yutong; Zhu, Ruoqing; Qu, Annie; Yeh, Mike

doi:10.1080/10618600.2022.2061496

Cited by 2 publications

(2 citation statements)

References 45 publications

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“…In Kruthika et al [ 27 ], autoencoders were used to extract features from 3D patches, obtaining a better result than when using traditional 2D patches. In [ 28 ], the authors show that the performance of the classification problem in case of dermoscopic analyses significantly outperforms that of raw images. Researchers have also used LSTM (long short-term memory) layers on top of three-dimensional convolutional layers to improve the performance of models.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease

Chakraborty

Zhuang

Xue

et al. 2023

Genes

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The prognosis and treatment of patients suffering from Alzheimer’s disease (AD) have been among the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify genetic variants associated with brain atrophy. Commonly, in these analyses, neuroimaging features are extracted based on one of many possible brain atlases with FreeSurf and other popular software; this, however, may cause the loss of important information due to our incomplete knowledge about brain function embedded in these suboptimal atlases. To address the issue, we propose convolutional neural network (CNN) models applied to three-dimensional MRI data for the whole brain or multiple, divided brain regions to perform completely data-driven and automatic feature extraction. These image-derived features are then used as endophenotypes in genome-wide association studies (GWASs) to identify associated genetic variants. When we applied this method to ADNI data, we identified several associated SNPs that have been previously shown to be related to several neurodegenerative/mental disorders, such as AD, depression, and schizophrenia.

show abstract

Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease

Chakraborty

Zhuang

Xue

et al. 2023

Genes

View full text Add to dashboard Cite

show abstract

“…In Kruthika et al [19], auto encoders were used to extract features from the 3D patches showing a better result than the traditional 2D patches. In [22], author shows that the performance of the classification problem in case of dermoscopic analyses outperforms that of the raw images significantly. Researchers have also used LSTM (long short term memory) layers on top of three dimensional convolutional layers to improve the performance of the models.…”

Section: Classification and Feature Extractionmentioning

confidence: 99%

Deep Learning-based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease

Chakraborty

Zhuang

Xue

et al. 2022

Preprint

View full text Add to dashboard Cite

The prognosis and treatment of the patients suffering from Alzheimer's disease (AD) have been one of the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify genetic variants associated with brain atrophy. Commonly in these analyses, neuroimaging features are extracted based on one of many possible brain atlases with FreeSurf and other popular softwares, which however may lose important information due to our incomplete knowledge about brain function embedded in these suboptimal atlases. To address the issue, we propose convolutional neural network (CNN) models applied to three-dimensional MRI data for the whole brain or multiple divided brain regions to perform completely data-driven and automatic feature extraction. These image-derived features are then used as endophenotypes in Genome-Wide Association Studies (GWAS) to identify associated genetic variants. When applied to the ADNI data, we identified several associated SNPs which have been previously shown to be related to several neurodegenerative/mental disorders such as AD, depression and schizophrenia. Code and supplementary materials are available at https://github.com/Dipnil07. The codes have been implemented using Python, R and Plink softwares.

show abstract

Dermoscopic Image Classification with Neural Style Transfer

Cited by 2 publications

References 45 publications

Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease

Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease

Deep Learning-based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease

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