Paper proposes CNN for diagnosing rare muscular dystrophies from images.
problem Challenges in diagnosing rare diseases due to lack of data.
method Uses a Convolutional Neural Network (CNN) for patch-based classification of confocal microscopy images.
result CNN system identifies problematic areas and provides global patient evaluation.
This paper studies a new Bayesian algorithm for the joint reconstruction and classification of reflectance confocal microscopy (RCM) images, with application to the identification of human skin lentigo. The proposed Bayesian approach takes advantage of the distribution of the multiplicative speckle noise affecting the …
Fluorescence microscopy has enabled a dramatic development in modern biology. Due to its inherently weak signal, fluorescence microscopy is not only much noisier than photography, but also presented with Poisson-Gaussian noise where Poisson noise, or shot noise, is the dominating noise source. To get clean fluorescence…
Deep learning improves 3D microscopy resolution without matched target images.
problem Anisotropic resolution in volumetric fluorescence microscopy.
method Cycle-consistent generative adversarial network trained on unpaired 2D images.
result Enhanced axial resolution and restored details between imaging planes.
The abstract discusses maximal rank 4-webs formed by confocal conics and their properties.
problem Characterizing confocal conics using 4-webs of maximal rank.
method Examining three different 4-webs formed by confocal conics and proving they are of maximal rank.
result Confocal conics are characterized from the web theory viewpoint.
We propose a discretization of classical confocal coordinates. It is based on a novel characterization thereof as factorizable orthogonal coordinate systems. Our geometric discretization leads to factorizable discrete nets with a novel discrete analog of the orthogonality property. A discrete confocal coordinate system…
We illustrate the theory of one-dimensional pluri-Lagrangian systems with the example of commuting billiard maps in confocal quadrics.
Confocal quadrics lie at the heart of the system of confocal coordinates (also called elliptic coordinates, after Jacobi). We suggest a discretization which respects two crucial properties of confocal coordinates: separability and all two-dimensional coordinate subnets being isothermic surfaces (that is, allowing a con…
The study constructs non-planar nets and webs on quadrics and Minkowski space.
problem Constructing non-planar nets and webs on quadrics and Minkowski space.
method Introducing canonical parametrisations, exploiting connections with classical deformations, and using Laguerre geometric notions.
result Existence and construction of octahedral grids and webs of surfaces.
We discuss Darboux-Staude type of thread configurations for the ellipsoid similar to Chasles-Graves type of thread configurations for the ellipse. These threads are formed by rectilinear segments, geodesic and line of curvature segments on the considered ellipsoid and with tangents tangent to the given ellipsoid and a …
A hyperkähler 4-metric with a triholomorphic SU(2) action gives rise to a family of confocal quadrics in Euclidean 3-space when cast in the canonical form of a hyperkähler 4-metric metric with a triholomorphic circle action. Moreover, at least in the case of geodesics orthogonal to the U(1) fibres, both the covariant S…
New CNN method improves deconvolution microscopy without PSF measurement.
problem Computational expense and blind estimation in conventional deconvolution microscopy.
method Cycle consistent CNN with explicit PSF modeling layers for blind deconvolution.
result Algorithm robustness and efficacy confirmed through experimental results.
Convolutional neural networks (CNN) have achieved state of the art performance on both classification and segmentation tasks. Applying CNNs to microscopy images is challenging due to the lack of datasets labeled at the single cell level. We extend the application of CNNs to microscopy image classification and segmentat…
Method estimates section thickness and XY anisotropy in ssEM images.
problem Accurate 3D reconstructions require precise section thickness and XY anisotropy estimates.
method Non-parametric Bayesian regression of image statistics.
result Method has lower estimation error compared to existing methods.
We discuss recently emerging applications of the state-of-art deep learning methods on optical microscopy and microscopic image reconstruction, which enable new transformations among different modes and modalities of microscopic imaging, driven entirely by image data. We believe that deep learning will fundamentally ch…
Unified method for multi-defect microscopy image restoration with limited training data.
problem Challenges in applying deep learning methods due to limited training data for multi-defect microscopy images.
method Two-stage approach: data augmentation with GAN and conditional GAN training.
result Proposed method gives comparable or superior results to existing methods in image quality restoration.
Confocal quadrics capture (encode) and geometrize spectral properties of symmetric operators. Certain metric-projective properties of confocal quadrics (most of them established in the first half of the XIXth century) {\it carry out} (stick and transfer) by rolling to and influence surfaces {\it applicabl…
A dataset of 10 molecule types for machine learning studies.
problem Lack of suitable datasets for machine learning in molecular imaging.
method Generated 2D cross-sectional projections of 10 molecule types from Molecular Dynamics trajectories.
result Benchmark dataset for machine learning, deep learning, and image processing in scattering, imaging, and microscopy.
Machine learning classifies colorectal tissue using photoacoustic microscopy.
problem Traditional diagnostic methods for colorectal cancer are limited in detail and painful.
method Machine learning applied to acoustic resolution photoacoustic microscopy.
result Machine learning accurately classified benign and malignant tissue.
Automated malaria diagnosis from field slides achieves accurate results.
problem Challenges in analyzing field-prepared thin blood film microscopy images.
method Fully automated framework using machine learning, including CNNs trained on diverse field samples.
result Results are close to sufficient for drug resistance monitoring and clinical use-cases.
DISPR uses diffusion models to predict 3D cell shapes from 2D images.
problem Predicting 3D cell shapes from 2D microscopy images.
method Diffusion model trained to predict 3D shapes from 2D microscopy images as a prior.
result Adding DISPR predictions to minority cell classes improves classification accuracy.
Lazy labels enable deep learning for microscopy segmentation without full annotation.
problem Lack of pixel-wise annotations limits fully supervised learning for bioimage segmentation.
method Lazy labels combined with coarse labels for training a deep neural network.
result Model achieves accurate segmentation with minimal pixel-wise annotations.
Machine learning speeds up FLIM analysis in biomedical research.
problem Complex, slow, and computationally expensive FLIM analysis.
method Machine learning techniques for faster and smarter FLIM data extraction and interpretation.
result Higher accuracy in classifying and segmenting FLIM images compared to conventional methods.
Multi-StyleGAN simulates live cell microscopy imagery.
problem Costly and complex live cell experiments.
method Generative adversarial network (GAN) synthesizing multi-domain time-lapse images.
result Captures biophysical factors and time dependencies in cell imagery.
We consider congruences of straight lines in a plane with the combinatorics of the square grid, with all elementary quadrilaterals possessing an incircle. It is shown that all the vertices of such nets (we call them incircular or IC-nets) lie on confocal conics. Our main new results are on checkerboard IC-nets in the p…
New method extracts biological concepts from cell microscopy images.
problem Extracting meaningful concepts from vision foundation models trained on cell microscopy images.
method Sparse dictionary learning (DL) combined with PCA whitening pre-processing.
result Successfully retrieved biologically meaningful concepts like cell types and genetic perturbations.
Study improves pollen detection in optical and holographic images using deep learning.
problem Improving pollen detection accuracy in holographic microscopy images.
method Used YOLOv8s for detection and MobileNetV3L for classification, addressing performance gaps through dataset expansion and automated labeling.
result Significant improvement in detection and classification performance on holographic images.
A deep model learns to infer fluorescence labels from unlabeled microscopy images.
problem Challenges in obtaining high quality images of cellular structures due to complex environments and label staining limitations.
method Developed a novel deep model using global pixel transformer layers and dense blocks, incorporating multi-scale input strategy.
result Significantly outperforms state-of-the-art methods in fluorescence image prediction tasks.
Prototype for adaptive electron microscopy scans reduces dose and time.
problem Reduce electron microscopy scan time and dose with minimal loss.
method Adaptive partial scanning with reinforcement learning.
result Reinforcement learning trained neural network optimizes scan paths.
UDVD uses deep learning to denoise videos without supervision.
problem Lack of clean video data for training deep learning models.
method UDVD is a CNN trained solely on noisy video data, adapting to local motion.
result UDVD performs as well as supervised methods, even with limited training data.
A novel method detects multiple mitosis events and mitigates annotation gaps in phase-contrast microscopy.
problem Detecting multiple mitosis events and handling annotation gaps in closely placed cells.
method Estimating a spatiotemporal likelihood map via 3DCNN to detect multiple mitosis events and mitigate annotation gaps.
result Our method outperformed compared methods in terms of F1-score using a challenging dataset.
Improves spline quality and accuracy in computational microscopy.
problem Detecting slender, overlapping structures in microscopy images.
method Differentiable rendering approach for spline refinement.
result Achieves high reliability and sub-pixel accuracy.
Deep learning improves single-molecule localization for super-resolution microscopy.
problem Accurate and efficient localization of single molecules for super-resolution microscopy.
method A novel deep learning network architecture that uses temporal context to simultaneously detect and localize molecules.
result Achieves state-of-the-art performance on the SMLM2016 challenge, excels at high densities.
This paper connects billiards in ellipses to focal billiards in ellipsoids.
problem Proving the existence of isometric counterparts between billiards in ellipses and focal billiards in ellipsoids.
method Continuous transition via isometric focal billiards in a fixed ellipsoid.
result Established the connection between planar and spatial billiards.
AI enhances pollen recognition in veterinary imaging using holographic microscopy.
problem Challenges in recognizing pollen in holographic images due to speckle noise and artifacts.
method Training YOLOv8s and MobileNetV3L on dual-modality dataset, employing WGAN-SN for synthetic data augmentation.
result GAN-based augmentation improves object detection and classification in holographic images, closing the performance gap.
Geometric proof of Regge symmetry in different geometries.
problem Regge symmetry in tetrahedra edge lengths.
method Simple geometric proof in Euclidean, spherical, and hyperbolic geometries.
result Verification of Regge symmetry across different geometries.
Cryo-electron microscopy (cryo-EM) is an emerging experimental method to characterize the structure of large biomolecular assemblies. Single particle cryo-EM records 2D images (so-called micrographs) of projections of the three-dimensional particle, which need to be processed to obtain the three-dimensional reconstruct…
We introduce a new discrete system that arises from ellipsoidal billiards and is closely related to the double reflection nets. The system is defined on the lattice of a uniform honeycomb consisting of rectified hypercubes and cross polytopes. In the 2-dimensional case, the lattice is regular and it incorporates dyna…
Develops statistical guarantees for image-to-image regression models.
problem Current image-to-image regression models lack statistical guarantees for model mistakes and hallucinations.
method Uncertainty quantification techniques with rigorous statistical guarantees for image-to-image regression problems.
result Derives uncertainty intervals around each pixel with formal mathematical guarantees.
We describe the geometry of geodesics on a Lorentz ellipsoid: give explicit formulas for the first integrals (pseudo-confocal coordinates), curvature, geodesically equivalent Riemannian metric, the invariant area-forms on the time- and space-like geodesics and invariant 1-form on the space of null geodesics. We prove a…
Deep learning reduces artifacts in limited angle X-ray microscopy.
problem Artifacts in image reconstruction from limited angle data in TXM.
method Training a U-Net deep neural network from synthetic data to reduce artifacts.
result Significant improvement in image quality and structural similarity.
New method separates objects from images using deep neural networks trained to inpaint.
problem Fully self-supervised instance separation of occluded objects in images.
method Maximizes independence of two image regions given a fully self-supervised inpainting network.
result Method achieves similar segmentation performance to fully supervised methods on microscopy image datasets.
Algorithm aligns 3D density maps using Wasserstein distance.
problem Aligning 3D density maps in cryogenic electron microscopy.
method Minimizing 1-Wasserstein distance after rigid transformation using Bayesian optimization.
result Improved accuracy and efficiency in protein molecule alignment.
This research uses Siamese networks to identify partial mouse brain images from the Allen atlas.
problem Identifying precise mouse brain microscopy images from the Allen atlas.
method Siamese Networks with contrastive learning to find corresponding atlas plates for partial images.
result Siamese CNNs achieved 25% TOP-1 and 100% TOP-5 accuracy in identifying brain slices from the Allen atlas.
Few-shot cell segmentation from diverse sources to target domain.
problem Efficient cell segmentation from limited annotated images.
method Meta-learning combining cross-domain tasks and invariant representation.
result Promising results from 1-10-shot learning on public databases.
Local minimax analysis for Poisson deconvolution of discrete signals.
problem Estimating a discrete uniform signal from Poisson convolutions.
method Local minimax risk analysis of a broad class of kernels.
result Sharp estimation rates as a function of signal clustering.
Novel co-learning method for manifolds with group actions using multiple fibre bundles.
problem Learning from manifolds with group actions without labeled data.
method Representation theory to associate multiple fibre bundles, leveraging group actions for unsupervised learning.
result Improved robust nearest neighbor search and community detection on cryo-electron microscopy images.
MuLANN tackles multi-domain learning with adversarial approach.
problem Automated microscopy data with domain bias.
method Semi-supervised multi-domain learning with MuLANN.
result Improves state of the art on image benchmarks and bioimage dataset.