Proposes a new network to improve nuclei segmentation in histopathology images.
problem Challenges in separating overlapped nuclei in histopathology images.
method Introduces a bending loss regularized network to minimize contour points with large curvatures.
result Outperforms six state-of-the-art approaches on five quantitative metrics.
A deep learning framework segments deep cerebellar nuclei from 7T MRI, improving accuracy and consistency.
problem Challenging to accurately segment small deep cerebellar nuclei using standard MRI protocols.
method Proposes DCN-Net, a deep learning framework that encodes contextual information and handles imbalanced data.
result Improves segmentation accuracy and consistency compared to existing methods.
Deep object detection improves mitotic nucleus detection in breast cancer biopsies.
problem Challenges in automated mitotic nucleus detection in breast cancer histopathological images.
method Adapted Mask R-CNN for deep object detection, initially selects candidate regions with maximum recall, refines them with multi-object loss function.
result Improved discrimination ability (F-score of 0.86) and significant precision (0.86) for mitotic nuclei compared to two-stage models.
Fast, accurate thalamus segmentation method for MS and ET.
problem Automated thalamic nuclei segmentation for neurological diseases.
method Cascaded multi-planar scheme with modified residual U-Net architecture.
result Statistically significant improvements in thalamus segmentation for MS and ET patients.
Deep neural networks improve cancer nuclei classification and survival analysis.
problem Accurate cell nuclei classification for cancer diagnosis and survival analysis.
method Implementation and evaluation of deep neural network models and ensembles for nuclei classification in RCC and PCa.
result Convolutional neural network system based on residual learning significantly improves cell nuclei classification.
New CNN approach detects cell nuclei with prior information.
problem Challenges in detecting cell nuclei due to image quality and morphology diversity.
method Develops SP-CNN with trainable shape prior layer to guide CNN learning.
result TSP-CNN outperforms state-of-the-art alternatives on challenging datasets.
NuClick uses clicks inside nuclei to improve nuclear segmentation.
problem Lack of efficient tools for nuclear segmentation due to labor-intensive annotation.
method Convolutional neural network framework that uses single point clicks for nuclei segmentation.
result NuClick generates superior segmentation results and facilitates more annotations.
RCCNet simplifies CNN for efficient colon cancer nuclei classification.
problem Efficient and precise classification of histological cell nuclei for medical analysis.
method Proposes RCCNet, a simplified CNN architecture with 1.5M parameters.
result Achieved 80.61% accuracy and 0.7887 F1 score on CRCHistoPhenotypes dataset.
This paper proposes synthetic augmentation for nuclei image segmentation in medical pathology.
problem Rare and time-consuming labeling of tumor nuclei images for semantic segmentation.
method Label-to-image translation to generate synthetic images.
result Synthetic augmentation improves segmentation accuracy.
Enhanced nuclei detection in cell images using shape priors with CNNs.
problem Challenges in detecting cell nuclei due to image quality and diversity of nuclear morphology.
method Developed SP-CNN, a new network structure incorporating learnable and fixed components guided by canonical shapes and a new regularization term.
result Experimental results show SP-CNN is competitive or outperforms state-of-the-art methods for nuclei detection.
A deep learning framework separates overlapping nuclei in histology images.
problem Challenges in nuclear segmentation due to overlapping nuclei.
method Proposal-free spatially-aware deep learning framework with multi-scale spatial information.
result State-of-the-art performance in nuclear segmentation on a multi-organ data set.
Based on the work of Durhuus-J{ó}nsson and Benedetti-Ziegler, we revisit the question of the number of triangulations of the 3-ball. We introduce a notion of nucleus (a triangulation of the 3-ball without internal nodes, and with each internal face having at most 1 external edge). We show that every triangulation can b…
NucleusDiff models atomic nuclei interactions to prevent separation violations in drug design.
problem Maintaining minimum pairwise distance between atoms to avoid separation violations in drug design.
method Enforces distance constraint between atomic nuclei and manifolds in a diffusion model.
result Reduces separation violations by up to 100.00% and enhances binding affinity by up to 22.16%.
New findings show many 3-balls are not Mogami.
problem Determining if all 3-balls are Mogami.
method Characterized Mogami property in terms of nuclei.
result Many 3-balls are not Mogami.
Fink AGN classifier achieves high accuracy in classifying active galactic nuclei.
problem Classifying active galactic nuclei from astronomical data.
method Built features from photometric points and color estimation. Used active learning for optimized training sample. Applied traditional machine learning algorithms.
result Achieved 98.0% accuracy in classifying real alerts from ZTF.
In this note we show that there are 4-manifolds not containing Gompf nucleus N2; in this way we answer Problem 4.98 of Kirby's problem list in the negative.
We introduce a new generalization of Gompf nuclei and give applications. We construct infinitely many exotic smooth structures for a large class of compact 4-manifolds with boundary, regarding topological invariants. We prove that a large class of closed 3-manifolds (including disjoint unions of Stein fillable 3-manifo…
New numerical methods for analyzing shapes of curves.
problem Discretization of Sobolev metrics on curve spaces.
method Developed algorithms for solving geodesic problems.
result Computed Karcher means and performed shape analysis.
The above named paper has been withdrawn. A colleague has observed a gap in the proof of isotopy invariance, which can be repaired by reducing the coefficients (which lie in (1/6)Z) of the antisymmetric kanji with chords incident with more than one component modulo 8Z. An analogous issue arises in considering the effec…
We consider few-body bound state systems and provide precise definitions of Borromean and Brunnian systems. The initial concepts are more than a hundred years old and originated in mathematical knot-theory as purely geometric considerations. About thirty years ago they were generalized and applied to the binding of sys…
An algorithm reduces breast cancer detection data complexity using effect sizes.
problem Improving accuracy in breast cancer detection.
method Statistical feature selection and SVM classifier with linear kernel.
result SVM classifier achieved over 90% accuracy.
Bayesian analysis predicts properties of proton-emitting nuclei beyond the proton drip line.
problem Predicting properties of unstable nuclei in the proton-rich region.
method Bayesian Gaussian processes and mass models corrected with statistical emulators.
result Quantified predictions for separation energies and probabilities of proton emission.
In the area of traditional physics the atomic nucleus belongs to the most complex systems. It involves essentially all elements that characterize complexity including the most distinctive one whose essence is a permanent coexistence of coherent patterns and of randomness. From a more interdisciplinary perspective, thes…
Bayesian methods improve nuclear mass predictions for unstable nuclei.
problem Improving predictions of nuclear masses far from stability.
method Bayesian Gaussian processes and neural networks applied to 10 models.
result Significant reduction in rms deviation from experimental data.
UNet++ improves medical image segmentation with deep supervision.
problem Improving accuracy in medical image segmentation.
method Nested U-Net architecture with deep supervision.
result UNet++ achieves significant improvements in IoU scores.
In a previous paper we have constructed an invariant of four-dimensional manifolds with boundary in the form of an element in the stable homotopy group of the Seiberg-Witten Floer spectrum of the boundary. Here we prove that when one glues two four-manifolds along their boundaries, the Bauer-Furuta invariant of the res…
In the recent years, Riemannian shape analysis of curves and surfaces has found several applications in medical image analysis. In this paper we present a numerical discretization of second order Sobolev metrics on the space of regular curves in Euclidean space. This class of metrics has several desirable mathematical …
This work speeds up DFT simulations using approximate Gaussian processes.
problem Slow DFT simulations due to large data sets.
method Approximate Gaussian processes (sparse variational GP, stochastic variational GP, deep kernel learned GP) to speed up DFT model predictions.
result Calibrated DFT models can predict properties of experimentally unobserved nuclides.
We compare correlations and coherent structures in nuclei and financial markets. In the nuclear physics part we review giant resonances which can be interpreted as a coherent structure embedded in chaos. With similar methods we investigate the financial empirical correlation matrix of the DAX and Dow Jones. We will sho…
Quantified limits of nuclear stability beyond drip lines.
problem Predicting nuclear stability beyond known isotopes.
method Microscopic nuclear mass models, Bayesian methodology, Gaussian processes.
result Quantified predictions of one- and two-nucleon separation energies.
Neural network improves breast cancer diagnosis with high accuracy.
problem Improving accuracy in breast cancer diagnosis.
method Higher-order probabilistic perceptron (HOPP) model.
result HOPP model achieves up to 97% accuracy in classifying breast cancer tumors.
Machine learning predicts nuclear physics parameters with high accuracy.
problem Predicting nuclear physics parameters for superheavy elements.
method Gradient boosted trees algorithm trained on nuclear data.
result Predictions have standard deviation from 0.00035 to 0.73.
Proposes RCVs for explaining deep neural network predictions in medical images.
problem Need for explainable predictions in medical applications.
method Uses continuous concept measures as RCVs in neural network activation space.
result Nuclei texture is a relevant concept in breast cancer grading.
Nuclear magnetic resonance (NMR) spectroscopy exploits the magnetic properties of atomic nuclei to discover the structure, reaction state and chemical environment of molecules. We propose a probabilistic generative model and inference procedures for NMR spectroscopy. Specifically, we use a weighted sum of trigonometric…
Bayesian optimization helps find best nuclear interaction parameters.
problem Finding best coupling constants in complex nuclear interaction models.
method Bayesian optimization applied to chiral effective field theory.
result Bayesian optimization performs well in low-dimensional parameter domains.
CNN identifies AGN host galaxies from Sloan Digital Sky Survey data.
problem Identifying AGN host galaxies using traditional methods is time-consuming.
method Trained a convolutional neural network on 210,000 galaxies.
result CNN can distinguish AGN host galaxies from non-active galaxies.
New invariant calculates 4-manifolds using trisection diagrams and combings.
problem Calculating non-semisimple 4-manifold invariants.
method Using trisection diagrams and combings of the trisection surface.
result Invariant calculated for Stein nuclei, generalizing earlier semisimple version.
CAggNet improves medical image segmentation by fusing coarse and fine features.
problem Medical image segmentation accuracy and efficiency.
method Crossing Aggregation Network with nested skip connections and weighted aggregation.
result CAggNet achieves more accurate and efficient segmentation compared to existing methods.
Set classification problems arise when classification tasks are based on sets of observations as opposed to individual observations. In set classification, a classification rule is trained with N sets of observations, where each set is labeled with class information, and the prediction of a class label is performed a…
Statistical modeling of nuclear data provides a novel approach to nuclear systematics complementary to established theoretical and phenomenological approaches based on quantum theory. Continuing previous studies in which global statistical modeling is pursued within the general framework of machine learning theory, we …
Generative Distribution Embeddings learn multiscale representations of distributions.
problem Learning representations of entire distributions for multiscale reasoning.
method Introducing GDE framework that lifts autoencoders to the space of distributions, using conditional generative models and distributional invariance.
result GDEs learn predictive sufficient statistics embedded in Wasserstein space, recovering distances and trajectories for Gaussian and Gaussian mixture distributions.
Kernelized SUSAN fuzzy clustering improves noisy image segmentation.
problem Inability of existing algorithms to preserve edge information and structural details.
method Kernelized Weighted SUSAN fuzzy C-Means clustering with spatial constraints and edge quality metric.
result Improved segmentation of noisy images with preserved edge information and structural details.
Bayesian model predicts drip-line locations in heavy calcium isotopes.
problem Determining the neutron drip line in the Ca region of heavy nuclei.
method Bayesian model averaging with Gaussian-process-based extrapolations.
result Predicted posterior probabilities for drip-line locations in heavy calcium isotopes.
Generative models create H&E-stained and destained prostate biopsy images.
problem Lack of H&E-stained prostate biopsy images.
method Conditional GAN for H&E staining, destaining model learning from stained to non-stained images.
result Generated images maintain structural similarity to non-stained biopsy.
The paper constructs non-smoothable actions on spin 4-manifolds.
problem Non-smoothability of Z/p-actions on indefinite spin 4-manifolds. method Constructs examples of non-smoothable actions using equivariant κ-invariants and calculations of η-invariants. result Non-smoothable actions remain non-smoothable under certain stabilizations.
Committee neural network models improve accuracy and enable active learning for interatomic potentials.
problem Improving accuracy and generalization error in interatomic potentials.
method Adapting committee models to neural networks, using multiple models with shared descriptors, and applying active learning to select configurations.
result Committee disagreement provides a measure of generalization error and guides active learning to minimize it.
A probabilistic approach classifies galaxies using spectroscopic data.
problem Classifying galaxies based on emission lines.
method Gaussian mixture model (GMM) applied to spectroscopic data.
result Four Gaussian components explain up to 97% of data variance.
Automated neural network potentials achieve coupled cluster accuracy for protonated water clusters.
problem Creating highly accurate potential energy surfaces for chemical systems.
method Automated fitting of neural network potentials to ab initio reference calculations.
result Single potential energy surface for H3O+ to H9O4+ clusters at essentially converged coupled cluster accuracy.