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Data Compression

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Relating observability and compressed sensing of time-varying signals in recurrent linear networks.

Neural networks : the official journal of the International Neural Network Society
In this paper, we study how the dynamics of recurrent networks, formulated as general dynamical systems, mediate the recovery of sparse, time-varying signals. Our formulation resembles the well-described problem of compressed sensing, but in a dynami...

Fast Multiclass Dictionaries Learning With Geometrical Directions in MRI Reconstruction.

IEEE transactions on bio-medical engineering
OBJECTIVE: Improve the reconstructed image with fast and multiclass dictionaries learning when magnetic resonance imaging is accelerated by undersampling the k-space data.

Scalable gastroscopic video summarization via similar-inhibition dictionary selection.

Artificial intelligence in medicine
OBJECTIVE: This paper aims at developing an automated gastroscopic video summarization algorithm to assist clinicians to more effectively go through the abnormal contents of the video.

RBF-network based sparse signal recovery algorithm for compressed sensing reconstruction.

Neural networks : the official journal of the International Neural Network Society
The approach of applying a cascaded network consisting of radial basis function nodes and least square error minimization block to Compressed Sensing for recovery of sparse signals is analyzed in this paper to improve the computation time and converg...

A Neurodynamic Optimization Method for Recovery of Compressive Sensed Signals With Globally Converged Solution Approximating to l0 Minimization.

IEEE transactions on neural networks and learning systems
Finding the optimal solution to the constrained l0 -norm minimization problems in the recovery of compressive sensed signals is an NP-hard problem and it usually requires intractable combinatorial searching operations for getting the global optimal s...

Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition.

Radiology. Artificial intelligence
Purpose To investigate whether the computational effort of three-dimensional CT-based multiorgan segmentation with TotalSegmentator can be reduced via Tucker decomposition-based network compression. Materials and Methods In this retrospective study, ...

AI for predicting chemical-effect associations at the chemical universe level-deepFPlearn.

Briefings in bioinformatics
Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemica...

LTH-ECG: Lottery Ticket Hypothesis-based Deep Learning Model Compression for Atrial Fibrillation Detection from Single Lead ECG On Wearable and Implantable Devices.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Atrial Fibrillation (AF) is a kind of arrhythmia, which is a major morbidity factor, and AF can lead to stroke, heart failure and other cardiovascular complications. Electrocardiogram (ECG) is the basic marker to test the condition of heart and it ca...

Uncovering the key dimensions of high-throughput biomolecular data using deep learning.

Nucleic acids research
Recent advances in high-throughput single-cell RNA-seq have enabled us to measure thousands of gene expression levels at single-cell resolution. However, the transcriptomic profiles are high-dimensional and sparse in nature. To address it, a deep lea...

Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis.

JCO clinical cancer informatics
PURPOSE: Deep learning (DL), a class of approaches involving self-learned discriminative features, is increasingly being applied to digital pathology (DP) images for tasks such as disease identification and segmentation of tissue primitives (eg, nucl...