Artificial Intelligence Medical Compendium

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

Showing 58,961 to 58,970 of 227,876 articles

FAIR-ESI: Feature Adaptive Importance Refinement for Electrophysiological Source Imaging

arXiv Jan 22, 2026
An essential technique for diagnosing brain disorders is electrophysiological source imaging (ESI). While model-based optimization and deep learning methods have achieved promising results in this field, the accurate selection and refinement of featu... read more 
Computer Vision and Pattern Recognition cs.AI

Sub-Region-Aware Modality Fusion and Adaptive Prompting for Multi-Modal Brain Tumor Segmentation

arXiv Jan 22, 2026
The successful adaptation of foundation models to multi-modal medical imaging is a critical yet unresolved challenge. Existing models often struggle to effectively fuse information from multiple sources and adapt to the heterogeneous nature of pathol... read more 
Computer Vision and Pattern Recognition

Breaking the Resolution Barrier: Arbitrary-resolution Deep Image Steganography Framework

arXiv Jan 22, 2026
Deep image steganography (DIS) has achieved significant results in capacity and invisibility. However, current paradigms enforce the secret image to maintain the same resolution as the cover image during hiding and revealing. This leads to two challe... read more 
Computer Vision and Pattern Recognition

White-Box mHC: Electromagnetic Spectrum-Aware and Interpretable Stream Interactions for Hyperspectral Image Classification

arXiv Jan 22, 2026
In hyperspectral image classification (HSIC), most deep learning models rely on opaque spectral-spatial feature mixing, limiting their interpretability and hindering understanding of internal decision mechanisms. We present physical spectrum-aware wh... read more 
Computer Vision and Pattern Recognition

Atlas-Assisted Segment Anything Model for Fetal Brain MRI (FeTal-SAM)

arXiv Jan 22, 2026
This paper presents FeTal-SAM, a novel adaptation of the Segment Anything Model (SAM) tailored for fetal brain MRI segmentation. Traditional deep learning methods often require large annotated datasets for a fixed set of labels, making them inflexibl... read more 
Computer Vision and Pattern Recognition cs.LG

LL-GaussianMap: Zero-shot Low-Light Image Enhancement via 2D Gaussian Splatting Guided Gain Maps

arXiv Jan 22, 2026
Significant progress has been made in low-light image enhancement with respect to visual quality. However, most existing methods primarily operate in the pixel domain or rely on implicit feature representations. As a result, the intrinsic geometric s... read more 
Computer Vision and Pattern Recognition

Rethinking Drug-Drug Interaction Modeling as Generalizable Relation Learning

arXiv Jan 22, 2026
Drug-drug interaction (DDI) prediction is central to drug discovery and clinical development, particularly in the context of increasingly prevalent polypharmacy. Although existing computational methods achieve strong performance on standard benchmark... read more 
Machine Learning q-bio.BM

LL-GaussianImage: Efficient Image Representation for Zero-shot Low-Light Enhancement with 2D Gaussian Splatting

arXiv Jan 22, 2026
2D Gaussian Splatting (2DGS) is an emerging explicit scene representation method with significant potential for image compression due to high fidelity and high compression ratios. However, existing low-light enhancement algorithms operate predominant... read more 
Computer Vision and Pattern Recognition

Diffusion Model-Based Data Augmentation for Enhanced Neuron Segmentation

arXiv Jan 22, 2026
Neuron segmentation in electron microscopy (EM) aims to reconstruct the complete neuronal connectome; however, current deep learning-based methods are limited by their reliance on large-scale training data and extensive, time-consuming manual annotat... read more 
Computer Vision and Pattern Recognition

Beyond Off-the-Shelf Models: A Lightweight and Accessible Machine Learning Pipeline for Ecologists Working with Image Data

arXiv Jan 22, 2026
We introduce a lightweight experimentation pipeline designed to lower the barrier for applying machine learning (ML) methods for classifying images in ecological research. We enable ecologists to experiment with ML models independently, thus they can... read more 
Computer Vision and Pattern Recognition cs.LG
Popular Topics
Recent Journals