AIMC Topic: Image Processing, Computer-Assisted

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Enhancing few-shot image classification through learnable multi-scale embedding and attention mechanisms.

Neural networks : the official journal of the International Neural Network Society
In the context of few-shot classification, the goal is to train a classifier using a limited number of samples while maintaining satisfactory performance. However, traditional metric-based methods exhibit certain limitations in achieving this objecti...

SHFormer: Dynamic spectral filtering convolutional neural network and high-pass kernel generation transformer for adaptive MRI reconstruction.

Neural networks : the official journal of the International Neural Network Society
Attention Mechanism (AM) selectively focuses on essential information for imaging tasks and captures relationships between regions from distant pixel neighborhoods to compute feature representations. Accelerated magnetic resonance image (MRI) reconst...

ADAMT: Adaptive distributed multi-task learning for efficient image recognition in Mobile Ad-hoc Networks.

Neural networks : the official journal of the International Neural Network Society
Distributed machine learning in mobile adhoc networks faces significant challenges due to the limited computational resources of devices, non-IID data distribution, and dynamic network topology. Existing approaches often rely on centralized coordinat...

DRTN: Dual Relation Transformer Network with feature erasure and contrastive learning for multi-label image classification.

Neural networks : the official journal of the International Neural Network Society
The objective of multi-label image classification (MLIC) task is to simultaneously identify multiple objects present in an image. Several researchers directly flatten 2D feature maps into 1D grid feature sequences, and utilize Transformer encoder to ...

Dual selective fusion transformer network for hyperspectral image classification.

Neural networks : the official journal of the International Neural Network Society
Transformer has achieved satisfactory results in the field of hyperspectral image (HSI) classification. However, existing Transformer models face two key challenges when dealing with HSI scenes characterized by diverse land cover types and rich spect...

ABVS breast tumour segmentation via integrating CNN with dilated sampling self-attention and feature interaction Transformer.

Neural networks : the official journal of the International Neural Network Society
Given the rapid increase in breast cancer incidence, the Automated Breast Volume Scanner (ABVS) is developed to screen breast tumours efficiently and accurately. However, reviewing ABVS images is a challenging task owing to the significant variations...

DNA-CBIR: DNA Translation Inspired Codon Pattern-Based Deep Image Feature Extraction for Content-Based Image Retrieval.

IEEE transactions on nanobioscience
DNA is emerging as a promising medium for storing huge volumes of data in a confined space that remains intact for thousands of years. Although this technique is very efficient, especially for multimedia data like images, there is a lack of efficient...

TUNeS: A Temporal U-Net With Self-Attention for Video-Based Surgical Phase Recognition.

IEEE transactions on bio-medical engineering
OBJECTIVE: To enable context-aware computer assistance in the operating room of the future, cognitive systems need to understand automatically which surgical phase is being performed by the medical team. The primary source of information for surgical...

Image debanding using cross-scale invertible networks with banded deformable convolutions.

Neural networks : the official journal of the International Neural Network Society
Banding artifacts in images stem from limitations in color bit depth, image compression, or over-editing, significantly degrades image quality, especially in regions with smooth gradients. Image debanding is about eliminating these artifacts while pr...

Machine learning-based multi-pool Voigt fitting of CEST, rNOE, and MTC in Z-spectra.

Magnetic resonance in medicine
PURPOSE: Four-pool Voigt (FPV) machine learning (ML)-based fitting for Z-spectra was developed to reduce fitting times for clinical feasibility in terms of on-scanner analysis and to promote larger cohort studies. The approach was compared to four-po...