29 research outputs found
Unsupervised non-parametric change point detection in quasi-periodic signals
We propose a new unsupervised and non-parametric method to detect change
points in intricate quasi-periodic signals. The detection relies on optimal
transport theory combined with topological analysis and the bootstrap
procedure. The algorithm is designed to detect changes in virtually any
harmonic or a partially harmonic signal and is verified on three different
sources of physiological data streams. We successfully find abnormal or
irregular cardiac cycles in the waveforms for the six of the most frequent
types of clinical arrhythmias using a single algorithm. The validation and the
efficiency of the method are shown both on synthetic and on real time series.
Our unsupervised approach reaches the level of performance of the supervised
state-of-the-art techniques. We provide conceptual justification for the
efficiency of the method and prove the convergence of the bootstrap procedure
theoretically.Comment: 8 pages, 7 figures, 1 tabl
BRUL\`E: Barycenter-Regularized Unsupervised Landmark Extraction
Unsupervised retrieval of image features is vital for many computer vision
tasks where the annotation is missing or scarce. In this work, we propose a new
unsupervised approach to detect the landmarks in images, validating it on the
popular task of human face key-points extraction. The method is based on the
idea of auto-encoding the wanted landmarks in the latent space while discarding
the non-essential information (and effectively preserving the
interpretability). The interpretable latent space representation (the
bottleneck containing nothing but the wanted key-points) is achieved by a new
two-step regularization approach. The first regularization step evaluates
transport distance from a given set of landmarks to some average value (the
barycenter by Wasserstein distance). The second regularization step controls
deviations from the barycenter by applying random geometric deformations
synchronously to the initial image and to the encoded landmarks. We demonstrate
the effectiveness of the approach both in unsupervised and semi-supervised
training scenarios using 300-W, CelebA, and MAFL datasets. The proposed
regularization paradigm is shown to prevent overfitting, and the detection
quality is shown to improve beyond the state-of-the-art face models.Comment: 10 main pages with 6 figures and 1 Table, 14 pages total with 6
supplementary figures. I.B. and N.B. contributed equally. D.V.D. is
corresponding autho
Global Adaptive Filtering Layer for Computer Vision
We devise a universal adaptive neural layer to "learn" optimal frequency
filter for each image together with the weights of the base neural network that
performs some computer vision task. The proposed approach takes the source
image in the spatial domain, automatically selects the best frequencies from
the frequency domain, and transmits the inverse-transform image to the main
neural network. Remarkably, such a simple add-on layer dramatically improves
the performance of the main network regardless of its design. We observe that
the light networks gain a noticeable boost in the performance metrics; whereas,
the training of the heavy ones converges faster when our adaptive layer is
allowed to "learn" alongside the main architecture. We validate the idea in
four classical computer vision tasks: classification, segmentation, denoising,
and erasing, considering popular natural and medical data benchmarks.Comment: 28 pages, 25 figures (main article and supplementary material). V.S.
and I.B contributed equally, D.V.D is Corresponding autho
Self-supervised Physics-based Denoising for Computed Tomography
Computed Tomography (CT) imposes risk on the patients due to its inherent
X-ray radiation, stimulating the development of low-dose CT (LDCT) imaging
methods. Lowering the radiation dose reduces the health risks but leads to
noisier measurements, which decreases the tissue contrast and causes artifacts
in CT images. Ultimately, these issues could affect the perception of medical
personnel and could cause misdiagnosis. Modern deep learning noise suppression
methods alleviate the challenge but require low-noise-high-noise CT image pairs
for training, rarely collected in regular clinical workflows. In this work, we
introduce a new self-supervised approach for CT denoising Noise2NoiseTD-ANM
that can be trained without the high-dose CT projection ground truth images.
Unlike previously proposed self-supervised techniques, the introduced method
exploits the connections between the adjacent projections and the actual model
of CT noise distribution. Such a combination allows for interpretable
no-reference denoising using nothing but the original noisy LDCT projections.
Our experiments with LDCT data demonstrate that the proposed method reaches the
level of the fully supervised models, sometimes superseding them, easily
generalizes to various noise levels, and outperforms state-of-the-art
self-supervised denoising algorithms.Comment: 13 pages, 12 figures. Under revie
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
Synthetic CT Generation from MRI Using Improved DualGAN
Synthetic CT image generation from MRI scan is necessary to create
radiotherapy plans without the need of co-registered MRI and CT scans. The
chosen baseline adversarial model with cycle consistency permits unpaired
image-to-image translation. Perceptual loss function term and coordinate
convolutional layer were added to improve the quality of translated images. The
proposed architecture was tested on paired MRI-CT dataset, where the synthetic
CTs were compared to corresponding original CT images. The MAE between the
synthetic CT images and the real CT scans is 61 HU computed inside of the true
CTs body shape