AI Medical Compendium Journal:
IEEE transactions on pattern analysis and machine intelligence

Showing 11 to 20 of 300 articles

Developmental Plasticity-Inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks.

IEEE transactions on pattern analysis and machine intelligence
Developmental plasticity plays a prominent role in shaping the brain's structure during ongoing learning in response to dynamically changing environments. However, the existing network compression methods for deep artificial neural networks (ANNs) an...

Multi-Sensor Learning Enables Information Transfer Across Different Sensory Data and Augments Multi-Modality Imaging.

IEEE transactions on pattern analysis and machine intelligence
Multi-modality imaging is widely used in clinical practice and biomedical research to gain a comprehensive understanding of an imaging subject. Currently, multi-modality imaging is accomplished by post hoc fusion of independently reconstructed images...

Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy.

IEEE transactions on pattern analysis and machine intelligence
Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative r...

Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures.

IEEE transactions on pattern analysis and machine intelligence
Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predictions. Different ML-based methods for protein-ligand binding affinity (PLA) prediction have different inductive biases, leading to different levels o...

An Audio-Visual Speech Separation Model Inspired by Cortico-Thalamo-Cortical Circuits.

IEEE transactions on pattern analysis and machine intelligence
Audio-visual approaches involving visual inputs have laid the foundation for recent progress in speech separation. However, the optimization of the concurrent usage of auditory and visual inputs is still an active research area. Inspired by the corti...

Deep Learning for Visual Speech Analysis: A Survey.

IEEE transactions on pattern analysis and machine intelligence
Visual speech, referring to the visual domain of speech, has attracted increasing attention due to its wide applications, such as public security, medical treatment, military defense, and film entertainment. As a powerful AI strategy, deep learning t...

EGCN++: A New Fusion Strategy for Ensemble Learning in Skeleton-Based Rehabilitation Exercise Assessment.

IEEE transactions on pattern analysis and machine intelligence
Skeleton-based exercise assessment focuses on evaluating the correctness or quality of an exercise performed by a subject. Skeleton data provide two groups of features (i.e., position and orientation), which existing methods have not fully harnessed....

A Multi-Level Interpretable Sleep Stage Scoring System by Infusing Experts' Knowledge Into a Deep Network Architecture.

IEEE transactions on pattern analysis and machine intelligence
In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translational challenges remain for user applications due to a lack of interpretability of algorithmic decis...

VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision.

IEEE transactions on pattern analysis and machine intelligence
Almost all digital videos are coded into compact representations before being transmitted. Such compact representations need to be decoded back to pixels before being displayed to humans and - as usual - before being enhanced/analyzed by machine visi...

A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation.

IEEE transactions on pattern analysis and machine intelligence
Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermin...