AIMC Topic: Data Compression

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S2LIC: Learned image compression with the SwinV2 block, Adaptive Channel-wise and Global-inter attention Context.

Neural networks : the official journal of the International Neural Network Society
Recently, deep learning technology has been successfully applied in the field of image compression, leading to superior rate-distortion performance. It is crucial to design an effective and efficient entropy model to estimate the probability distribu...

Progressive fine-to-coarse reconstruction for accurate low-bit post-training quantization in vision transformers.

Neural networks : the official journal of the International Neural Network Society
Due to its efficiency, Post-Training Quantization (PTQ) has been widely adopted for compressing Vision Transformers (ViTs). However, when quantized into low-bit representations, there is often a significant performance drop compared to their full-pre...

Human visual perception-inspired medical image segmentation network with multi-feature compression.

Artificial intelligence in medicine
Medical image segmentation is crucial for computer-aided diagnosis and treatment planning, directly influencing clinical decision-making. To enhance segmentation accuracy, existing methods typically fuse local, global, and various other features. How...

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...

A Portable Ultrasound Imaging System Utilizing Deep Generative Learning-Based Compressive Sensing On Pre-Beamformed RF Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Recent advances in the unsupervised and generative models of deep learning have shown promise for application in biomedical signal processing. In this work, we present a portable resource-constrained ultrasound (US) system trained using Variational A...

Knowledge-driven dictionaries for sparse representation of continuous glucose monitoring signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Continuous glucose monitoring (CGM) of patients with diabetes allows the effective management of the disease and reduces the risk of hypoglycemic or hyperglycemic episodes. Towards this goal, the development of reliable CGM models is essential for re...