Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 64,991 to 65,000 of 231,605 articles

Implicit neural network-based coal SEM super-resolution for enhancing micro-pores measurement tasks.

Neural networks : the official journal of the International Neural Network Society Jan 10, 2026
Prolonged radiation exposure in coal Scanning Electron Microscopy (SEM) poses structural damage risks to specimens during high-resolution observation. To mitigate this situation, we propose an interactive-interpretable super-resolution (SR) framework... read more 

DH-MSVM: A hybrid algorithm for seeking quality support vectors in distributed learning.

Neural networks : the official journal of the International Neural Network Society Jan 10, 2026
Data heterogeneity is a common yet complex challenge in distributed machine learning scenarios. However, current Distributed Support Vector Machines (DSVMs) lack effective mechanisms to identify suitable support vectors across diverse data structures... read more 

A causal bidirectional selective state space model for imaging genetics in neurodegenerative diseases.

Neural networks : the official journal of the International Neural Network Society Jan 10, 2026
Brain imaging genetics aims to uncover the pathological mechanisms and improve the diagnosis of brain diseases, particularly neurodegenerative disorders. While deep learning has advanced feature extraction and association modeling in this field, ther... read more 

Self-supervised exceptional prototypical network for few-shot grading of gastric intestinal metaplasia.

Neural networks : the official journal of the International Neural Network Society Jan 10, 2026
Automatic grading of Gastric Intestinal Metaplasia (GIM) is valuable in assisting the diagnosis of early gastric cancer. Recently, prototypical networks are served as a effective method for medical image processing in few-shot scenarios. However, exi... read more 

AdaAlign: A unified solution for traditional and modern zero-shot sketch-based image retrieval.

Neural networks : the official journal of the International Neural Network Society Jan 10, 2026
Zero-shot sketch-based image retrieval (ZS-SBIR) is challenging due to the cross-domain nature of sketches and photos, as well as the semantic gap between seen and unseen classes. With the rapid advancements in modern large vision-language models (VL... read more 

Exploring cognitive workload recognition using CogRepLKNet with EEG-fMRI.

Neural networks : the official journal of the International Neural Network Society Jan 10, 2026
Accurate multimodal Cognitive Workload Recognition (CWR) remains challenging due to the difficulty of modeling cross-modal relationships between Electroencephalography (EEG) and Functional Magnetic Resonance Imaging (fMRI) data. Additionally, the inh... read more 

A two-stage active cleaning strategy for long-tail label noise.

Neural networks : the official journal of the International Neural Network Society Jan 10, 2026
Long-tailed data is ubiquitous in real-world applications, posing significant challenges due to imbalanced class distribution and high levels of label noise. Previous methods to address long-tailed data with label noise often incur high computational... read more 

Key-value pair-free continual learner via task-specific prompt-prototype.

Neural networks : the official journal of the International Neural Network Society Jan 10, 2026
Continual learning aims to enable models to acquire new knowledge while retaining previously learned information. Prompt-based methods have shown remarkable performance in this domain; however, they typically rely on key-value pairing, which can intr... read more 

An online forecasting-based fine-tuning pipeline for time-series anomaly prediction.

Neural networks : the official journal of the International Neural Network Society Jan 10, 2026
Time-series anomaly detection is critical for numerous real-world applications and has been extensively studied. However, existing methods are typically designed to identify anomalies within a complete time series. In other words, they rely on access... read more 

YOFOR : You only focus on object regions for tiny object detection in aerial images.

Neural networks : the official journal of the International Neural Network Society Jan 10, 2026
With development of deep learning methods, performance of object detection has been greatly improved. However, the high resolution of remotely sensed images, the complexity of the background, the uneven distribution of objects, and the uneven number ... read more 
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