Dataset Information

Machine learning in spectral domain.

ABSTRACT: Deep neural networks are usually trained in the space of the nodes, by adjusting the weights of existing links via suitable optimization protocols. We here propose a radically new approach which anchors the learning process to reciprocal space. Specifically, the training acts on the spectral domain and seeks to modify the eigenvalues and eigenvectors of transfer operators in direct space. The proposed method is ductile and can be tailored to return either linear or non-linear classifiers. Adjusting the eigenvalues, when freezing the eigenvectors entries, yields performances that are superior to those attained with standard methods restricted to operate with an identical number of free parameters. To recover a feed-forward architecture in direct space, we have postulated a nested indentation of the eigenvectors. Different non-orthogonal basis could be employed to export the spectral learning to other frameworks, as e.g. reservoir computing.

SUBMITTER: Giambagli L

PROVIDER: S-EPMC7910623 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Machine learning in spectral domain.

Giambagli Lorenzo L Buffoni Lorenzo L Carletti Timoteo T Nocentini Walter W Fanelli Duccio D

Nature communications 20210226 1

Deep neural networks are usually trained in the space of the nodes, by adjusting the weights of existing links via suitable optimization protocols. We here propose a radically new approach which anchors the learning process to reciprocal space. Specifically, the training acts on the spectral domain and seeks to modify the eigenvalues and eigenvectors of transfer operators in direct space. The proposed method is ductile and can be tailored to return either linear or non-linear classifiers. Adjust ...[more]

PMID: 33637729

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Project description:PurposeTo develop the first normative database of macular and circumpapillary scans with reference values at the level of the A-scan using the Heidelberg Spectralis Optical Coherence Tomography (OCT) machine.MethodsThis study is a retrospective cross sectional analysis of macular and circumpapillary OCT scans of healthy individuals. All participants had a full ophthalmic examination, including best corrected visual acuity, intraocular pressure, biomicroscopy, posterior segment examination and OCT scan. The volume and thickness of each of the nine Early Treatment Diabetic Retinopathy zones at the macula were analysed for the total retinal thickness, retinal nerve fibre layer (RNFL), ganglion cell layer (GCL) and inner plexiform layer (IPL). The thickness of the circumpapillary RNFL was analysed at the disc. Associations between age, gender, refractive error and OCT measurements were explored. De-identified A-scans were extracted from the OCT machine as separate tab-separated text file and made available according to the data sharing statement.ResultsTwo-hundred eyes from 146 participants were included of which 69 (47%) were female. The mean age (SD) was 48.52 (17.52). Participants were evenly distributed across four age groups and represented nine broad ethnic groups in proportions comparable to the local distribution. All the macular scans were 20° x 20° (5.9 mm x 5.9 mm), with a total scan density between 12,800 and 49,152 A-scans. The peripapillary scans were all 12° (3.5 mm), at a scan density of 768 A-scans. The mean retinal, GCL and IPL volumes were significantly greater in males than females. Mean peripapillary RNFL thickness did not differ significantly between males and females. Age and total retinal volume (r = -0.2561, P = 0.0003), GCL volume (-0.2911, P < 0.0001) and IPL volume (-0.3194, P < 0.0001) were negatively correlated. The IPL had the strongest three significant negatively associated segments; superior inner IPL (r = -0.3444, P < 0.0001), nasal outer IPL (r = -0.3217, P < 0.0001) and inferior inner IPL (r = -0.3179, P < 0.0001). The temporal inner macular RNFL showed a statistically significant positive correlation (r = 0.1929, P = 0.0062) with age. The only significant association between age and thickness at the peripapillary disc scan was the superior temporal sector (r = -0.1910, P = 0.0067). All retinal layers were negatively correlated for refractive error, except for the central RNFL which was positively correlated (r = 0.1426, P = 0.044).ConclusionThis study provides a normative database of macular and circumpapillary scans with reference values at the level of the A-scan using the Heidelberg Spectralis Optical Coherence Tomography (OCT) machine.

Dataset Information

Machine learning in spectral domain.

Publications

Machine learning in spectral domain.

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