AI generates retinal blood flow maps from standard OCT images.
problem Limited expert-generated labels in medical imaging.
method Training AI on OCTA images to infer perfusion from structural OCT.
result AI-generated maps have similar fidelity to OCTA and better than expert clinicians.
DONE algorithm optimizes unknown functions with noisy measurements.
problem Online optimization of unknown functions with costly and noisy measurements.
method Uses a random Fourier expansion to maintain a surrogate function and iteratively update it with new measurements.
result DONE algorithm is significantly faster than Bayesian optimization while achieving similar or better performance.
Study compares handcrafted and deep neural network features for OCT image classification.
problem Classifying OCT images for disease detection.
method Comparison of Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP), DenseNet-169, and ResNet50.
result Deep neural network methods outperform handcrafted features with higher accuracy and better underrepresented class performance.
DeepCap automates coronary artery segmentation from IVOCT images.
problem Automated segmentation of coronary arteries from IVOCT images is challenging.
method Developed a deep learning method based on capsules for robust, unbiased segmentation.
result DeepCap achieves segmentation quality comparable to state-of-the-art methods.
Deep neural networks improve margin assessment of breast tissue from OCT images.
problem Margin assessment of human breast tissue from OCT images.
method Used deep neural networks with function norm regularization.
result Significantly better results than other techniques, reducing EER from 12% to 5%.
This paper uses deep learning to estimate flow fields from OCT images for laser ablation control.
problem Automatic control of laser bone ablation using 4D OCT images.
method Semi-supervised convolutional neural network for 2.5D scene flow estimation.
result Scene flow estimation enables markerless tracking and automated laser ablation control.
The problem of using observed correlations to infer causal relations is relevant to a wide variety of scientific disciplines. Yet given correlations between just two classical variables, it is impossible to determine whether they arose from a causal influence of one on the other or a common cause influencing both, unle…
Deep learning improves breast cancer detection in DOT.
problem Complex physics and ill-posedness in DOT reconstruction.
method Deep learning approach that learns non-linear photon scattering physics.
result Deep neural network accurately recovers optical anomalies.
Score-based models improve diffuse optical tomography accuracy.
problem Improving accuracy in diffuse optical tomography with uncertainty quantification.
method Score-based diffusion models with a mixed score function to prevent overfitting.
result Data-driven prior distribution results in posterior samples with low variance and centred around the ground truth.
Deep learning predicts diabetic macular edema from fundus photos.
problem Diabetic macular edema diagnosis from fundus photos is inaccurate.
method Trained deep learning model on color fundus photographs.
result Deep learning model has higher sensitivity and PPV than human specialists.
Paper uses deep Q-network to correct eye movement artifacts in OCT volumes.
problem Unsupervised correction of inter-frame misalignments in OCT volumes due to eye movement artifacts.
method Dueling deep Q-network trained to maximize reward signals based on intensity-based image similarity metrics.
result Average normalized mutual information and correlation coefficient of 0.985 and 0.914, respectively.
This study investigates transfer learning for medical image classification.
problem Limited data for training deep neural networks in medical domains.
method Transfer learning using various DNNs for diabetic retinopathy and macular edema.
result Transfer learning is feasible and promising for medical image classification.
Deep learning detects glaucoma from raw OCT volumes, outperforming classical methods.
problem Glaucoma diagnosis from OCT measurements using traditional features.
method 3D Convolutional Neural Network (CNN) for unsegmented OCT volumes.
result Deep learning achieved higher accuracy (AUC 0.94) compared to logistic regression (AUC 0.89).
Study on deep learning for speckle noise reduction in imaging modalities.
problem Multiplicative speckle noise challenges conventional deep learning methods for speckle denoising.
method Likelihood-based deep neural network (DNN) estimators for nonparametric regression under speckle noise.
result Established minimax rates for speckle denoising, matching those for additive Gaussian noise alone.
This paper explores deep learning for improving X-ray CT image reconstruction from undersampled data.
problem Improving image reconstruction from undersampled X-ray CT data.
method Analysis of classical and deep learning methods for solving inverse problems.
result Deep learning methods show promise in improving image quality from undersampled data.
Designs chiral photonic structures using machine learning for efficient optical properties.
problem Optimizing chiral photonic nanostructures for light-matter interactions.
method Evolutionary algorithm and neural network approach for rapid optimization.
result Frequency-dependent modification in reflected light's degree of circular polarization.
New method for partial matching of shapes with Varifolds.
problem Matching structures with topological or shape differences.
method Varifold shape representation and LDDMM framework.
result Effective partial matching despite topological differences.
Machine learning improves network analysis and self-management in optical communications.
problem Complexity and data heterogeneity in optical networks require advanced mathematical tools.
method Application of Machine Learning techniques to analyze and manage network data.
result Machine learning enables automated network self-configuration and fault management.
Framework learns asymmetric and local features in multi-dimensional data.
problem Learning features in multi-dimensional data, especially images.
method Bayesian hierarchical modeling with recursive wavelet transforms.
result Framework achieves high computational scalability and adaptivity.
We prove uniqueness results for a Calderon type inverse problem for the Hodge Laplacian acting on graded forms on certain manifolds in three dimensions. In particular, we show that partial measurements of the relative-to-absolute or absolute-to-relative boundary value maps uniquely determine a zeroth order potential. T…
Statistical inference and information processing of high-dimensional data often require efficient and accurate estimation of their second-order statistics. With rapidly changing data, limited processing power and storage at the acquisition devices, it is desirable to extract the covariance structure from a single pass …
LSTM neural networks improve fiber nonlinearities in coherent systems.
problem Compensating fiber nonlinearities in digital coherent systems.
method Utilization of Long short-term memory (LSTM) neural networks.
result LSTM neural networks provide superior performance compared to digital back propagation, especially in multi-channel scenarios.
Bayesian U-Net exploits epistemic uncertainty for anomaly detection in retinal OCT images.
problem Anomaly detection in retinal OCT images using weak labels of healthy anatomy.
method Bayesian U-Net trained on weak labels of healthy anatomy, using Monte Carlo dropout for uncertainty estimation, and post-processing to transfer uncertainty to anomaly segmentations.
result Achieved a Dice index of 0.789 in an independent test set of AMD cases.
New AI algorithm improves multi-layer optical film design efficiency.
problem Traditional algorithms converge to local optima, limiting global optimal solutions.
method Deep Q-learning for global optimal multi-layer optical film design.
result Deep Q-learning model converges global optimum of optical thin film structure.
Study travel time tomography for transversely isotropic media using modified pseudodifferential calculus.
problem Travel time tomography problem for transversely isotropic media.
method Modified scattering pseudodifferential calculus to solve the tomography problem.
result Construction and use of modified pseudodifferential calculus to solve the tomography problem.
Lecture notes on analysis tools for X-ray tomography.
problem Understanding X-ray tomography using mathematical analysis.
method Overview of analysis tools and ideas, minimal assumptions.
result Broad overview of analysis tools for X-ray tomography.
Study on learning quantum dynamics without direct interaction.
problem Learning quantum dynamics incoherently without direct interaction.
method Analyze sample complexity and prove bounds for incoherent learning.
result Prove that arbitrary measurements allow efficient learning of unitary processes incoherently.
Two machine learning applications for IP/Optical networks: traffic prediction and optical path performance.
problem Agile resource management and optical path performance prediction in IP/Optical networks.
method Machine learning for traffic prediction and optical performance prediction using SDN controllers.
result Efficient implementation of SDN controllers for agile resource management and optical path performance prediction.
Bayesian optical flow estimates motion with uncertainty quantification.
problem Ill-posed inverse problem of optical flow.
method Statistical approach treating optical flow as a statistical inverse problem.
result Bayesian optical flow provides statistical estimates of motion and uncertainty.
Deep learning classifies OCT images of normal vs AMD with high accuracy.
problem Automated classification of OCT images for diagnosing Age-related Macular Degeneration.
method Automated extraction of OCT images linked to clinical data, training a deep neural network.
result Deep learning achieved high accuracy (92.64% sensitivity, 93.69% specificity) in classifying OCT images of normal vs AMD.
Summary of tensor tomography proofs on manifolds with boundaries.
problem Proving injectivity of tensor tomography on compact Riemannian manifolds with boundaries.
method Summarized proofs from previous studies.
result Summary of proofs for s-injectivity.
Open problems in billiards and optics from a workshop.
problem Open problems in billiards and geometric optics.
method Collection of open problems from a workshop.
result No specific key result mentioned; collection of problems.
This work improves optic disc and cup segmentation for glaucoma detection.
problem Automatic segmentation of optic disc and cup on eye fundus images for glaucoma diagnosis.
method Modification of U-Net convolutional neural network.
result Our method achieves comparable quality to state-of-the-art methods, with faster prediction times.
New method generates optical vortices in any knot shape.
problem Generating optical vortices in complex shapes beyond simple knots.
method Mathematical construction and experimental verification.
result Complex optical fields can form any knot shape.
Paper addresses travel time tomography stability and statistical inversion.
problem Determining conformal factors of metrics from geodesic lengths.
method Established forward and inverse stability estimates; applied to Bayesian statistical inversion.
result Consistency of statistical inversion technique for travel time tomography.
Paper tackles depth estimation and optic disc-cup segmentation from color fundus images.
problem Depth estimation and optic disc-cup segmentation from color fundus images.
method Uses fully convolutional networks for monocular retinal depth estimation and optic disc-cup segmentation.
result Demonstrates improved accuracy in depth estimation and optic disc-cup segmentation.
Method detects and locates eavesdropping in optical links.
problem Detect and locate eavesdropping in optical links with small power losses.
method Cluster-based approach using OPM data at receiver and in-line OPM data for localization.
result Subtle eavesdropping losses can be detected and localized using OPM data.
Deep learning enhances optical microscopy and image reconstruction.
problem Improving image data transformations in optical microscopy.
method Application of deep learning methods on optical microscopy and image reconstruction.
result Deep learning enables new transformations among different modes and modalities of microscopic imaging.
Optical ESNs enable flexible, efficient machine learning with reduced energy.
problem Implementing universal computational capabilities in machine learning.
method Optical implementation of ESNs leveraging stimulated Brillouin scattering.
result Efficient, scalable, and memory-capable optical reservoir computing.
GANPOP uses deep learning to estimate optical properties from single images, improving accuracy over existing methods.
problem Estimating optical properties from single wide-field images.
method Conditional generative adversarial networks trained on paired images and optical property maps.
result GANPOP estimates optical properties with 58% higher accuracy than single-snapshot optical property technique in human gastrointestinal specimens.
Paper develops a deforestation detection system using optical and SAR data.
problem Detecting tree-loss in dense forests using satellite data.
method Combines optical and SAR data, uses KL expansion for anomaly detection, and Hidden Markov Model for classification.
result Hybrid method achieves high accuracy and robustness in sparse optical data.
Study of optical geometries with intrinsic torsion in general relativity.
problem Understanding null line distributions and their properties in Lorentzian manifolds.
method Investigation of intrinsic torsion and congruences of null curves, extending to generalized optical geometries.
result Characterization of conformal properties of null line distributions and congruences.
Optical DNNet boosts accuracy with multiple frequency-channels.
problem Improving the accuracy of optical neural networks.
method Developed a novel optical diffractive deep neural network with multiple frequency-channels.
result Multiple frequency-channels significantly increase network accuracy.
Study uses machine learning to solve photoacoustic tomography's inverse problem.
problem Solving the full inverse problem in photoacoustic tomography.
method Developed an approach using variational autoencoders for Bayesian estimation of the posterior distribution.
result Evaluated the approach with numerical simulations and compared it to a Bayesian solution.
Paper develops machine learning to translate SAR to optical images for easier interpretation.
problem Difficulty in human interpretation of SAR images due to non-adapted human vision to microwave scattering.
method Develops a novel reciprocal GAN scheme to train machine intelligence on co-registered SAR and optical images.
result The proposed translation network works well under various SAR and optical image resolutions and polarizations.
Express Wavenet reduces neural network parameters to 1% of standard networks.
problem Optical neural networks with high parameter count.
method Wavelet modulation, random shift wavelets, expressway structure.
result Express Wavenet achieves high accuracy with significantly fewer parameters.
Novel video prediction method for complex urban scenes using optical flow.
problem Making accurate future frame predictions in complex urban scenes.
method Optical flow conditioned method using video sequences and optical flow sequences.
result Empirical evaluations show the effectiveness of the method on KITTI and Cityscapes datasets.
Sketch Tomography improves quantum state estimation accuracy.
problem Efficiently estimating quantum states, especially MPS states.
method Hybridizes classical shadow protocol with tensor train ansatz.
result Proven convergence with quadratic sample complexity.