108 research outputs found
Landmarks Augmentation with Manifold-Barycentric Oversampling
The training of Generative Adversarial Networks (GANs) requires a large
amount of data, stimulating the development of new augmentation methods to
alleviate the challenge. Oftentimes, these methods either fail to produce
enough new data or expand the dataset beyond the original manifold. In this
paper, we propose a new augmentation method that guarantees to keep the new
data within the original data manifold thanks to the optimal transport theory.
The proposed algorithm finds cliques in the nearest-neighbors graph and, at
each sampling iteration, randomly draws one clique to compute the Wasserstein
barycenter with random uniform weights. These barycenters then become the new
natural-looking elements that one could add to the dataset. We apply this
approach to the problem of landmarks detection and augment the available
annotation in both unpaired and in semi-supervised scenarios. Additionally, the
idea is validated on cardiac data for the task of medical segmentation. Our
approach reduces the overfitting and improves the quality metrics beyond the
original data outcome and beyond the result obtained with popular modern
augmentation methods.Comment: 11 pages, 4 figures, 3 tables. I.B. and N.B. contributed equally.
D.V.D. is the corresponding autho
From Hypergraph Energy Functions to Hypergraph Neural Networks
Hypergraphs are a powerful abstraction for representing higher-order
interactions between entities of interest. To exploit these relationships in
making downstream predictions, a variety of hypergraph neural network
architectures have recently been proposed, in large part building upon
precursors from the more traditional graph neural network (GNN) literature.
Somewhat differently, in this paper we begin by presenting an expressive family
of parameterized, hypergraph-regularized energy functions. We then demonstrate
how minimizers of these energies effectively serve as node embeddings that,
when paired with a parameterized classifier, can be trained end-to-end via a
supervised bilevel optimization process. Later, we draw parallels between the
implicit architecture of the predictive models emerging from the proposed
bilevel hypergraph optimization, and existing GNN architectures in common use.
Empirically, we demonstrate state-of-the-art results on various hypergraph node
classification benchmarks. Code is available at
https://github.com/yxzwang/PhenomNN.Comment: Accepted to ICML 202
A Topological Deep Learning Framework for Neural Spike Decoding
The brain's spatial orientation system uses different neuron ensembles to aid
in environment-based navigation. One of the ways brains encode spatial
information is through grid cells, layers of decked neurons that overlay to
provide environment-based navigation. These neurons fire in ensembles where
several neurons fire at once to activate a single grid. We want to capture this
firing structure and use it to decode grid cell data. Understanding,
representing, and decoding these neural structures require models that
encompass higher order connectivity than traditional graph-based models may
provide. To that end, in this work, we develop a topological deep learning
framework for neural spike train decoding. Our framework combines unsupervised
simplicial complex discovery with the power of deep learning via a new
architecture we develop herein called a simplicial convolutional recurrent
neural network (SCRNN). Simplicial complexes, topological spaces that use not
only vertices and edges but also higher-dimensional objects, naturally
generalize graphs and capture more than just pairwise relationships.
Additionally, this approach does not require prior knowledge of the neural
activity beyond spike counts, which removes the need for similarity
measurements. The effectiveness and versatility of the SCRNN is demonstrated on
head direction data to test its performance and then applied to grid cell
datasets with the task to automatically predict trajectories
A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning
In this paper we present a method for simultaneously segmenting brain tumors
and an extensive set of organs-at-risk for radiation therapy planning of
glioblastomas. The method combines a contrast-adaptive generative model for
whole-brain segmentation with a new spatial regularization model of tumor shape
using convolutional restricted Boltzmann machines. We demonstrate
experimentally that the method is able to adapt to image acquisitions that
differ substantially from any available training data, ensuring its
applicability across treatment sites; that its tumor segmentation accuracy is
comparable to that of the current state of the art; and that it captures most
organs-at-risk sufficiently well for radiation therapy planning purposes. The
proposed method may be a valuable step towards automating the delineation of
brain tumors and organs-at-risk in glioblastoma patients undergoing radiation
therapy.Comment: corrected one referenc
Adaptive and Topological Deep Learning with applications to Neuroscience
Deep Learning and neuroscience have developed a two way relationship with each informing the other. Neural networks, the main tools at the heart of Deep Learning, were originally inspired by connectivity in the brain and have now proven to be critical to state-of-the-art computational neuroscience methods. This dissertation explores this relationship, first, by developing an adaptive sampling method for a neural network-based partial different equation solver and then by developing a topological deep learning framework for neural spike decoding. We demonstrate that our adaptive scheme is convergent and more accurate than DGM -- as long as the residual mirrors the local error -- at the same number of training steps and using the same or less number of training points. We present a multitude of tests applied to selected PDEs discussing the robustness of our scheme.
Next, we further illustrate the partnership between deep learning and neuroscience by decoding neural activity using a novel neural network architecture developed to exploit the underlying connectivity of the data by employing tools from Topological Data Analysis. Neurons encode information like external stimuli or allocentric location by generating firing patterns where specific ensembles of neurons fire simultaneously for one value. Understanding, representing, and decoding these neural structures require models that encompass higher order connectivity than traditional graph-based models may provide. Our framework combines unsupervised simplicial complex discovery with the power of deep learning via a new architecture we develop herein called a simplicial convolutional recurrent neural network (SCRNN). Simplicial complexes, topological spaces that use not only vertices and edges but also higher-dimensional objects, naturally generalize graphs and capture more than just pairwise relationships. The effectiveness and versatility of the SCRNN is demonstrated on head direction data to test its performance and then applied to grid cell datasets with the task to automatically predict trajectories
Filter-Based Probabilistic Markov Random Field Image Priors: Learning, Evaluation, and Image Analysis
Markov random fields (MRF) based on linear filter responses are one of the most popular forms for modeling image priors due to their rigorous probabilistic interpretations and versatility in various applications. In this dissertation, we propose an application-independent method to quantitatively evaluate MRF image priors using model samples. To this end, we developed an efficient auxiliary-variable Gibbs samplers for a general class of MRFs with flexible potentials. We found that the popular pairwise and high-order MRF priors capture image statistics quite roughly and exhibit poor generative properties. We further developed new learning strategies and obtained high-order MRFs that well capture the statistics of the inbuilt features, thus being real maximum-entropy models, and other important statistical properties of natural images, outlining the capabilities of MRFs. We suggest a multi-modal extension of MRF potentials which not only allows to train more expressive priors, but also helps to reveal more insights of MRF variants, based on which we are able to train compact, fully-convolutional restricted Boltzmann machines (RBM) that can model visual repetitive textures even better than more complex and deep models.
The learned high-order MRFs allow us to develop new methods for various real-world image analysis problems. For denoising of natural images and deconvolution of microscopy images, the MRF priors are employed in a pure generative setting. We propose efficient sampling-based methods to infer Bayesian minimum mean squared error (MMSE) estimates, which substantially outperform maximum a-posteriori (MAP) estimates and can compete with state-of-the-art discriminative methods. For non-rigid registration of live cell nuclei in time-lapse microscopy images, we propose a global optical flow-based method. The statistics of noise in fluorescence microscopy images are studied to derive an adaptive weighting scheme for increasing model robustness. High-order MRFs are also employed to train image filters for extracting important features of cell nuclei and the deformation of nuclei are then estimated in the learned feature spaces. The developed method outperforms previous approaches in terms of both registration accuracy and computational efficiency
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