AI Medical Compendium Journal:
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

Showing 1 to 10 of 191 articles

Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Ultrasound Localization Microscopy (ULM) is a non-invasive technique that allows for the imaging of micro-vessels in vivo, at depth and with a resolution on the order of ten microns. ULM is based on the sub-resolution localization of individual micro...

Adaptive Dual-Axis Style-Based Recalibration Network With Class-Wise Statistics Loss for Imbalanced Medical Image Classification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Salient and small lesions (e.g., microaneurysms on fundus) both play significant roles in real-world disease diagnosis under medical image examinations. Although deep neural networks (DNNs) have achieved promising medical image classification perform...

Iris Geometric Transformation Guided Deep Appearance-Based Gaze Estimation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
The geometric alterations in the iris's appearance are intricately linked to the gaze direction. However, current deep appearance-based gaze estimation methods mainly rely on latent feature sharing to leverage iris features for improving deep represe...

Global Cross-Entropy Loss for Deep Face Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Contemporary deep face recognition techniques predominantly utilize the Softmax loss function, designed based on the similarities between sample features and class prototypes. These similarities can be categorized into four types: in-sample target si...

VSR-Net: Vessel-Like Structure Rehabilitation Network With Graph Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
The morphologies of vessel-like structures, such as blood vessels and nerve fibres, play significant roles in disease diagnosis, e.g., Parkinson's disease. Although deep network-based refinement segmentation and topology-preserving segmentation metho...

Rethinking Copy-Paste for Consistency Learning in Medical Image Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Semi-supervised learning based on consistency learning offers significant promise for enhancing medical image segmentation. Current approaches use copy-paste as an effective data perturbation technique to facilitate weak-to-strong consistency learnin...

Sparse Coding Inspired LSTM and Self-Attention Integration for Medical Image Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Accurate and automatic segmentation of medical images plays an essential role in clinical diagnosis and analysis. It has been established that integrating contextual relationships substantially enhances the representational ability of neural networks...

Searching Discriminative Regions for Convolutional Neural Networks in Fundus Image Classification With Genetic Algorithms.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Deep convolutional neural networks (CNNs) have been widely used for fundus image classification and have achieved very impressive performance. However, the explainability of CNNs is poor because of their black-box nature, which limits their applicati...

Facial Action Unit Representation Based on Self-Supervised Learning With Ensembled Priori Constraints.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Facial action units (AUs) focus on a comprehensive set of atomic facial muscle movements for human expression understanding. Based on supervised learning, discriminative AU representation can be achieved from local patches where the AUs are located. ...

IdeNet: Making Neural Network Identify Camouflaged Objects Like Creatures.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Camouflaged objects often blend in with their surroundings, making the perception of a camouflaged object a more complex procedure. However, most neural-network-based methods that simulate the visual information processing pathway of creatures only r...