As a successful case of combining deep learning with photonics,the research on optical machine learning has recently undergone rapid development.Among various optical classification frameworks,diffractive networks hav...As a successful case of combining deep learning with photonics,the research on optical machine learning has recently undergone rapid development.Among various optical classification frameworks,diffractive networks have been shown to have unique advantages in all-optical reasoning.As an important property of light,the orbital angular momentum(OAM)of light shows orthogonality and mode-infinity,which can enhance the ability of parallel classification in information processing.However,there have been few all-optical diffractive networks under the OAM mode encoding.Here,we report a strategy of OAM-encoded diffractive deep neural network(OAM-encoded D2NN)that encodes the spatial information of objects into the OAM spectrum of the diffracted light to perform all-optical object classification.We demonstrated three different OAM-encoded D2NNs to realize(1)single detector OAM-encoded D2NN for single task classification,(2)single detector OAM-encoded D2NN for multitask classification,and(3)multidetector OAM-encoded D2NN for repeatable multitask classification.We provide a feasible way to improve the performance of all-optical object classification and open up promising research directions for D2NN by proposing OAMencoded D2NN.展开更多
BACKGROUND Optical coherence tomography(OCT)enables high-resolution,non-invasive visualization of retinal structures.Recent evidence suggests that retinal layer alterations may reflect central nervous system changes a...BACKGROUND Optical coherence tomography(OCT)enables high-resolution,non-invasive visualization of retinal structures.Recent evidence suggests that retinal layer alterations may reflect central nervous system changes associated with psychiatric disorders such as schizophrenia(SZ).AIM To develop an advanced deep learning model to classify OCT images and distinguish patients with SZ from healthy controls using retinal biomarkers.METHODS A novel convolutional neural network,Self-AttentionNeXt,was designed by integrating grouped self-attention mechanisms,residual and inverted bottleneck blocks,and a final 1×1 convolution for feature refinement.The model was trained and tested on both a custom OCT dataset collected from patients with SZ and a publicly available OCT dataset(OCT2017).RESULTS Self-AttentionNeXt achieved 97.0%accuracy on the collected SZ OCT dataset and over 95%accuracy on the public OCT2017 dataset.Gradient-weighted class activation mapping visualizations confirmed the model’s attention to clinically relevant retinal regions,suggesting effective feature localization.CONCLUSION Self-AttentionNeXt effectively combines transformer-inspired attention mechanisms with convolutional neural networks architecture to support the early and accurate detection of SZ using OCT images.This approach offers a promising direction for artificial intelligence-assisted psychiatric diagnostics and clinical decision support.展开更多
基金supported by the National Key Research and Development Program of China(Grant Nos.2021YFB2800604,2021YFB2800302,and 2018YFB2200403)the National Natural Science Foundation of China(Grant Nos.12274478,91950204,and 92150302)the Graduate Research and Practice Projects of Minzu University of China.
文摘As a successful case of combining deep learning with photonics,the research on optical machine learning has recently undergone rapid development.Among various optical classification frameworks,diffractive networks have been shown to have unique advantages in all-optical reasoning.As an important property of light,the orbital angular momentum(OAM)of light shows orthogonality and mode-infinity,which can enhance the ability of parallel classification in information processing.However,there have been few all-optical diffractive networks under the OAM mode encoding.Here,we report a strategy of OAM-encoded diffractive deep neural network(OAM-encoded D2NN)that encodes the spatial information of objects into the OAM spectrum of the diffracted light to perform all-optical object classification.We demonstrated three different OAM-encoded D2NNs to realize(1)single detector OAM-encoded D2NN for single task classification,(2)single detector OAM-encoded D2NN for multitask classification,and(3)multidetector OAM-encoded D2NN for repeatable multitask classification.We provide a feasible way to improve the performance of all-optical object classification and open up promising research directions for D2NN by proposing OAMencoded D2NN.
文摘BACKGROUND Optical coherence tomography(OCT)enables high-resolution,non-invasive visualization of retinal structures.Recent evidence suggests that retinal layer alterations may reflect central nervous system changes associated with psychiatric disorders such as schizophrenia(SZ).AIM To develop an advanced deep learning model to classify OCT images and distinguish patients with SZ from healthy controls using retinal biomarkers.METHODS A novel convolutional neural network,Self-AttentionNeXt,was designed by integrating grouped self-attention mechanisms,residual and inverted bottleneck blocks,and a final 1×1 convolution for feature refinement.The model was trained and tested on both a custom OCT dataset collected from patients with SZ and a publicly available OCT dataset(OCT2017).RESULTS Self-AttentionNeXt achieved 97.0%accuracy on the collected SZ OCT dataset and over 95%accuracy on the public OCT2017 dataset.Gradient-weighted class activation mapping visualizations confirmed the model’s attention to clinically relevant retinal regions,suggesting effective feature localization.CONCLUSION Self-AttentionNeXt effectively combines transformer-inspired attention mechanisms with convolutional neural networks architecture to support the early and accurate detection of SZ using OCT images.This approach offers a promising direction for artificial intelligence-assisted psychiatric diagnostics and clinical decision support.
