This paper simplifies complex geometry for shape analysis.
problem Understanding interactions between differential geometry and functional analysis.
method Provides an overview of infinite-dimensional Riemannian manifolds and metrics.
result Roadmap for beginners in computational anatomy and shape analysis.
The space of embedded submanifolds plays an important role in applications such as computational anatomy and shape analysis. We can define two different classes on Riemannian metrics on this space: so-called outer metrics are metrics that measure shape changes using deformations of the ambient space and they find appli…
These lecture notes explain the geometry and discuss some of the analytical questions underlying image registration within the framework of large deformation diffeomorphic metric mapping (LDDMM) used in computational anatomy.
Improved MRI head anatomy segmentation using deep learning with multiple priors.
problem Challenges in segmenting head anatomy in MRI, especially with lesions.
method Added three types of prior information to a 3D convolutional network: spatial priors, morphological priors, and spatial context.
result Multiprior network improves segmentation performance, especially for abnormal anatomies.
MosaicMRI expands public datasets for musculoskeletal MRI, revealing cross-anatomical correlations.
problem Limited diversity in public MRI datasets hinders model evaluation across different anatomical settings.
method Developed a large, diverse dataset (MosaicMRI) and conducted experiments on a baseline model (VarNet).
result Models trained on combined anatomies outperform anatomy-specific models in low-sample regimes.
Deep learning has been widely accepted as a promising solution for medical image segmentation, given a sufficiently large representative dataset of images with corresponding annotations. With ever increasing amounts of annotated medical datasets, it is infeasible to train a learning method always with all data from scr…
CRC method provides tighter uncertainty intervals for CT images.
problem Expressing uncertainty in CT images in clinically meaningful terms.
method Semantically adaptive CRC procedure leveraging length minimization.
result Valid coverage of ground-truth images with tighter uncertainty intervals.
Novel deep learning framework for mandible segmentation and landmarking.
problem Challenging problem of mandible segmentation and anatomical landmarking from CBCT scans.
method Three-step approach: deep neural network for segmentation, geodesic space for landmarks, LSTM for closely spaced landmarks.
result Superior efficacy compared to state-of-the-art methods in craniofacial anomalies and diseased states.
FalconBC improves patient-specific cardiovascular modeling by estimating boundary conditions efficiently.
problem Efficiently estimating boundary conditions in patient-specific cardiovascular models, especially in open-loop models and anatomies with lesions.
method A general amortized inference framework based on probabilistic flow that treats clinical targets and anatomies as conditioning variables.
result Demonstrated on two patient-specific models, FalconBC improves efficiency and accuracy in estimating boundary conditions.
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.
This work is an endeavor to develop a deep learning methodology for automated anatomical labeling of a given region of interest (ROI) in brain computed tomography (CT) scans. We combine both local and global context to obtain a representation of the ROI. We then use Relation Networks (RNs) to predict the corresponding …
Motivated by applications in computational anatomy, we consider a second-order problem in the calculus of variations on object manifolds that are acted upon by Lie groups of smooth invertible transformations. This problem leads to solution curves known as Riemannian cubics on object manifolds that are endowed with norm…
New method locates heart arrhythmia points quickly and reliably.
problem Locating a point-source heart arrhythmia using catheter data.
method Developed a nonconvex feasibility problem and optimization algorithm.
result Robust and fast localization of arrhythmia sources without prior anatomy knowledge.
DeepMRSeg uses deep learning for brain tissue segmentation.
problem Automated segmentation of brain tissues and abnormalities.
method Modified UNet architecture with multi-scale feature extraction.
result DeepMRSeg achieves high accuracy on various brain segmentation tasks.
Self-supervised method estimates depth from monocular endoscopy videos.
problem Depth estimation from monocular endoscopy data without manual labeling.
method Convolutional neural networks trained with sparse supervision from stereo methods.
result Submillimeter mean residual error in cross-patient CT scans comparison.
Finding the most effective way to aggregate multi-subject fMRI data is a long-standing and challenging problem. It is of increasing interest in contemporary fMRI studies of human cognition due to the scarcity of data per subject and the variability of brain anatomy and functional response across subjects. Recent work o…
Researchers develop methods to reduce simulation costs for cardiovascular modeling.
problem High computational cost of high-fidelity simulations in cardiovascular modeling.
method Use low-fidelity approximations, neural networks, and normalizing flows to construct surrogates.
result Validated methods reduce computational cost while maintaining accuracy.
A novel score decouples shape deformations for better shape analysis.
problem High-dimensional deformations absorb lower-dimensional components, affecting statistical analysis.
method Introduces a coupling score using varifold representation of vector fields to quantify and decouple deformation modes.
result The coupling score effectively decouples distinct deformation modes during registration, improving shape analysis.
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.
Market portfolio decomposed into body and tail legs
problem Separation of market portfolio into body and tail legs
method Dynamic value-weighted body and tail legs
result Recombination identity holds for all models
Deep learning predicts AF recurrence from MRI images without pre-processing.
problem Estimating AF recurrence from MRI images efficiently and accurately.
method Deep learning approach to predict shape descriptors directly from MRI images.
result Deep learning method produces similar outcomes to state-of-the-art methods.
A new FFT-based method for fast rigid alignment of 2D closed curves.
problem Rigid alignment of 2D closed curves with application to shape analysis.
method FFT-based algorithm for optimal rigid alignment of closed curves with O(N log N) complexity.
result Order of magnitude speed-up in curve alignment compared to previous methods.
This paper introduces the concept of functional current as a mathematical framework to represent and treat functional shapes, i.e. sub-manifold supported signals. It is motivated by the growing occurrence, in medical imaging and computational anatomy, of what can be described as geometrico-functional data, that is a da…
Study quantifies events leading to Terra project failure in 2022.
problem Terra project's fragility and dependency on Anchor protocol.
method Systematic review of social media news, hourly and transaction data analysis, network science techniques.
result Identified trigger events and analyzed dependency structures using network science.
Study decomposes market portfolio into body and tail legs, revealing systematic differences.
problem Understanding the relationship between body and tail components in market portfolios.
method Decomposes CRSP market portfolio into body and tail legs, analyzes their recombination identity.
result Recombination identity holds for all models but not for all, indicating systematic differences.
Lung segmentation accuracy varies little across diverse datasets.
problem Limited clinical applicability of automated lung segmentation methods.
method Comparison of four deep learning approaches and two standard algorithms on diverse datasets.
result Standard U-net approach yields higher accuracy on routine imaging data.
Tests factor models by decomposing market into body and tail legs, revealing inconsistent results.
problem Inconsistency between factor models and market behavior.
method Decomposes market into body and tail legs, testing factor models at daily and monthly frequencies.
result q5 model shows inconsistent results, with negative body and positive tail alphas at all split ratios.
To mitigate potential contagion from future banking crises, the European Commission recently proposed a framework which would provide for the bail-in of bank creditors in the event of failure. In this study, we examine this framework retrospectively in the context of failed European banks during the global f…
Machine learning builds personalized pediatric abdominal phantoms.
problem Lack of individualized phantoms for radiation therapy studies.
method Combining imaging and machine learning to automatically build phantoms.
result Automatically constructed phantoms more accurately represent patient anatomy.
Deep learning detects Crohn's disease in MRI images.
problem Detecting Crohn's disease in MRI images.
method Residual Networks for feature extraction and soft attention mechanisms for interpretability.
result Deep learning algorithms perform comparably to clinical standards with faster inference.
Probabilistic method identifies Purkinje network from ECG data.
problem Challenging task of identifying Purkinje conduction system in heart.
method Bayesian optimization and approximate Bayesian computation for probabilistic identification.
result Generates a population of plausible Purkinje networks fitting ECG within tolerance.
Back cover text: Megaprojects and Risk provides the first detailed examination of the phenomenon of megaprojects. It is a fascinating account of how the promoters of multibillion-dollar megaprojects systematically and self-servingly misinform parliaments, the public and the media in order to get projects approved and b…
China Vanke Co. faced a hostile takeover by Baoneng Group, sparking controversy.
problem A hostile takeover of China Vanke Co. by Baoneng Group.
method No specific method mentioned in the abstract.
result National controversy over corporate governance and government role in capital markets.
Real Estate Investment Trusts (REITs) are the only truly liquid assets related to real estate investments. We study the behavior of U.S. REITs over the past three decades and document their return characteristics. REITs have somewhat less market risk than equity; their betas against a broad market index average about .…
The paper extends LDDMM framework to include Lie group actions in large deformation shape registration.
problem Modeling smooth, invertible transformations between shapes using Lie groups and diffeomorphisms.
method Develops a registration model that decouples the actions of Lie groups and diffeomorphisms, using semidirect products and right-invariant sub-Riemannian structures.
result Joint optimization over both deformation groups improves registration accuracy and disentangles contributions.
Despite our extensive knowledge of biophysical properties of neurons, there is no commonly accepted algorithmic theory of neuronal function. Here we explore the hypothesis that single-layer neuronal networks perform online symmetric nonnegative matrix factorization (SNMF) of the similarity matrix of the streamed data. …
Novel method embeds generative model into Bayesian optimization for HD cardiac model parameter estimation.
problem High-dimensional optimization of patient-specific cardiac model parameters with limited data.
method Embeds a generative variational auto-encoder into the objective function of Bayesian optimization.
result Improves accuracy of parameter estimation with more than 10x gain in efficiency.
Synthesizing images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New images can then be generated by sampling a suitable parameter space. In this work, we propose a method that learns to synthesize eye fundus images directly from da…
In this paper, we address the problem of orientation that naturally arises when representing shapes like curves or surfaces as currents. In the field of computational anatomy, the framework of currents has indeed proved very efficient to model a wide variety of shapes. However, in such approaches, orientation of shapes…
Canonical correlation analysis (CCA) is a valuable method for interpreting cross-covariance across related datasets of different dimensionality. There are many potential applications of CCA to neuroimaging data analysis. For instance, CCA can be used for finding functional similarities across fMRI datasets collected fr…
FRODO method rejects out-of-distribution chest x-ray images with high accuracy.
problem Rejecting out-of-distribution samples in medical image analysis.
method Uses feature activations and Mahalanobis distance to measure distribution mismatch.
result Achieves an AUC score of 0.99 in classifying chest x-ray images as in or out of distribution.
LOUPE optimizes MRI under-sampling patterns for faster scans.
problem Accelerating MRI scans while maintaining image quality.
method End-to-end learning framework that trains on full-resolution scans.
result LOUPE-optimized masks yield superior reconstructions with 8x faster scans.
This paper investigates how forgetting affects neural network representations and stabilizes deeper layers.
problem Catastrophic forgetting in machine learning models trained on sequential tasks.
method Representational analysis techniques and empirical studies on CIFAR-10 and CIFAR-100 datasets.
result Deeper layers are disproportionately the source of forgetting, and methods to mitigate forgetting stabilize these layers.
New method aligns brain surfaces based on functional signatures.
problem Inter-individual variability in neuroimaging data.
method Fused Unbalanced Gromov-Wasserstein (FUGW) based on Optimal Transport.
result FUGW significantly increases between-subject correlation of activity.
Study reveals patterns in crypto-markets and predicts pump-and-dump schemes.
problem Understanding and predicting pump-and-dump activities in cryptocurrency markets.
method Empirical case study, data analysis, machine learning model building.
result Highly precise and robust machine learning model for predicting pump-and-dump events.
Logistic regression outperforms other models in radiology report classification.
problem Efficiently labeling radiology reports for model training.
method Simple machine learning models, including logistic regression, for binary and multiclass classification.
result Logistic regression binary classifier achieves above 0.9 average precision in unseen reports.
New model extracts shared brain activity patterns from fMRI data.
problem Challenges in aggregating multi-subject fMRI data due to variability.
method Shared Gaussian Process Factor Analysis (S-GPFA) incorporating temporal information.
result Model reveals ground truth latent structures and replicates experimental performance.
Deep learning improves radiotherapy by automating head and neck cancer organ segmentation.
problem Manual delineation of radio-sensitive organs in head and neck cancer radiotherapy is time-consuming and variably performed.
method A 3D U-Net deep learning architecture trained on clinical CT scans and expert segmentations.
result Deep learning model achieves expert-level performance in delineating 21 distinct OARs.