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Found 31 papers, 13 papers with code
Suppressing Modulation Instability with Reinforcement Learning
Modulation instability is a phenomenon of spontaneous pattern formation in nonlinear media, oftentimes leading to an unpredictable behaviour and a degradation of a signal of interest.
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.
QUASAR: QUality and Aesthetics Scoring with Advanced Representations
This paper introduces a new data-driven, non-parametric method for image quality and aesthetics assessment, surpassing existing approaches and requiring no prompt engineering or fine-tuning.
ENOT: Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport
We present a new extension for Neural Optimal Transport (NOT) training procedure, capable of accurately and efficiently estimating optimal transportation plan via specific regularisation on conjugate potentials.
Align Your Intents: Offline Imitation Learning via Optimal Transport
We report that AILOT outperforms state-of-the art offline imitation learning algorithms on D4RL benchmarks and improves the performance of other offline RL algorithms in the sparse-reward tasks.
FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures
The proposed solution has been evaluated using the DRIVE, CHASE-DB1, and STARE datasets, and competitive results are obtained when compared with previous studies.
DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images
In recent years, computer-aided diagnosis systems have shown great potential in assisting radiologists with accurate and efficient medical image analysis.
Ranked #4 on
Object Detection
on GRAZPEDWRI-DX
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.
Medical Image Captioning via Generative Pretrained Transformers
The proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO.
PyTorch Image Quality: Metrics for Image Quality Assessment
Image Quality Assessment (IQA) metrics are widely used to quantitatively estimate the extent of image degradation following some forming, restoring, transforming, or enhancing algorithms.
Feather-Light Fourier Domain Adaptation in Magnetic Resonance Imaging
Generalizability of deep learning models may be severely affected by the difference in the distributions of the train (source domain) and the test (target domain) sets, e. g., when the sets are produced by different hardware.
Image Quality Assessment for Magnetic Resonance Imaging
Moreover, the selection of these IQA metrics for a specific task typically involves intentionally induced distortions, such as manually added noise or artificial blurring; yet, the chosen metrics are then used to judge the output of real-life computer vision models.
Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging
Image corruption by motion artifacts is an ingrained problem in Magnetic Resonance Imaging (MRI).
DASHA: Decentralized Autofocusing System with Hierarchical Agents
State-of-the-art object detection models are frequently trained offline using available datasets, such as ImageNet: large and overly diverse data that are unbalanced and hard to cluster semantically.
Optimal MRI Undersampling Patterns for Ultimate Benefit of Medical Vision Tasks
To accelerate MRI, the field of compressed sensing is traditionally concerned with optimizing the image quality after a partial undersampling of the measurable $\textit{k}$-space.
Data Augmentation with Manifold Barycenters
Oftentimes, these methods either fail to produce enough new data or expand the dataset beyond the original knowledge domain.
Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution
We went below the MRI acceleration factors (a. k. a., k-space undersampling) reported by all published papers that reference the original fastMRI challenge, and then considered powerful deep learning based image enhancement methods to compensate for the underresolved images.
No-reference denoising of low-dose CT projections
Low-dose computed tomography (LDCT) became a clear trend in radiology with an aspiration to refrain from delivering excessive X-ray radiation to the patients.
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates
Annotating training data for sequence tagging of texts is usually very time-consuming.
Tubular Shape Aware Data Generation for Semantic Segmentation in Medical Imaging
Chest X-ray is one of the most widespread examinations of the human body.
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.
Deep learning Framework for Mobile Microscopy
Mobile microscopy is a promising technology to assist and to accelerate disease diagnostics, with its widespread adoption being hindered by the mediocre quality of acquired images.
LORCK: Learnable Object-Resembling Convolution Kernels
Segmentation of certain hollow organs, such as the bladder, is especially hard to automate due to their complex geometry, vague intensity gradients in the soft tissues, and a tedious manual process of the data annotation routine.
Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders
To address this problem, we introduce a new powerful method of image anomaly detection.
Deep Negative Volume Segmentation
Clinical examination of three-dimensional image data of compound anatomical objects, such as complex joints, remains a tedious process, demanding the time and the expertise of physicians.
BRULÈ: Barycenter-Regularized Unsupervised Landmark Extraction
Unsupervised retrieval of image features is vital for many computer vision tasks where the annotation is missing or scarce.
Reinforcement Learning Framework for Deep Brain Stimulation Study
Malfunctioning neurons in the brain sometimes operate synchronously, reportedly causing many neurological diseases, e. g. Parkinson's.
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.
Microscopy Image Restoration with Deep Wiener-Kolmogorov filters
Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes; yet, the observed images typically suffer from blur and background noise.
Reinforcement learning for suppression of collective activity in oscillatory ensembles
We present a use of modern data-based machine learning approaches to suppress self-sustained collective oscillations typically signaled by ensembles of degenerative neurons in the brain.
Deep Learning Super-Diffusion in Multiplex Networks
We show that modern machine learning architectures, such as fully connected and convolutional neural networks, can classify and predict the presence of super-diffusion in multiplex networks with 94. 12\% accuracy.
