Artificial Intelligence Medical Compendium

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

Showing 55,831 to 55,840 of 226,731 articles

SSNAPS: Audio-Visual Separation of Speech and Background Noise with Diffusion Inverse Sampling

arXiv Feb 1, 2026
This paper addresses the challenge of audio-visual single-microphone speech separation and enhancement in the presence of real-world environmental noise. Our approach is based on generative inverse sampling, where we model clean speech and ambient no... read more 
Audio and Speech Processing cs.LG cs.SD

Where to Attend: A Principled Vision-Centric Position Encoding with Parabolas

arXiv Feb 1, 2026
We propose Parabolic Position Encoding (PaPE), a parabola-based position encoding for vision modalities in attention-based architectures. Given a set of vision tokens-such as images, point clouds, videos, or event camera streams-our objective is to e... read more 
Computer Vision and Pattern Recognition cs.LG

DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data

arXiv Feb 1, 2026
Multivariate time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-term fluctuations, complicating causal inference under non-stationarity and autocorrelation. Existing ca... read more 
Machine Learning cs.AI stat.ML

BioTamperNet: Affinity-Guided State-Space Model Detecting Tampered Biomedical Images

arXiv Feb 1, 2026
We propose BioTamperNet, a novel framework for detecting duplicated regions in tampered biomedical images, leveraging affinity-guided attention inspired by State Space Model (SSM) approximations. Existing forensic models, primarily trained on natural... read more 
Computer Vision and Pattern Recognition

Theoretical Analysis of Measure Consistency Regularization for Partially Observed Data

arXiv Feb 1, 2026
The problem of corrupted data, missing features, or missing modalities continues to plague the modern machine learning landscape. To address this issue, a class of regularization methods that enforce consistency between imputed and fully observed dat... read more 
Machine Learning stat.ML

A texture-based framework for foundational ultrasound models

arXiv Feb 1, 2026
Ultrasound is the most widely used medical imaging modality, yet the images it produces are fundamentally unique, arising from tissue-dependent scattering, reflection, and speed-of-sound variations that produce a constrained set of characteristic tex... read more 
Image and Video Processing cs.CV

Rectified LpJEPA: Joint-Embedding Predictive Architectures with Sparse and Maximum-Entropy Representations

arXiv Feb 1, 2026
Joint-Embedding Predictive Architectures (JEPA) learn view-invariant representations and admit projection-based distribution matching for collapse prevention. Existing approaches regularize representations towards isotropic Gaussian distributions, bu... read more 
Machine Learning cs.CV

Learning to Guide Local Search for MPE Inference in Probabilistic Graphical Models

arXiv Feb 1, 2026
Most Probable Explanation (MPE) inference in Probabilistic Graphical Models (PGMs) is a fundamental yet computationally challenging problem arising in domains such as diagnosis, planning, and structured prediction. In many practical settings, the gra... read more 
Artificial Intelligence cs.LG

Data Augmentation for High-Fidelity Generation of CAR-T/NK Immunological Synapse Images

arXiv Feb 1, 2026
Chimeric antigen receptor (CAR)-T and NK cell immunotherapies have transformed cancer treatment, and recent studies suggest that the quality of the CAR-T/NK cell immunological synapse (IS) may serve as a functional biomarker for predicting therapeuti... read more 
Computer Vision and Pattern Recognition

SAGE: Agentic Framework for Interpretable and Clinically Translatable Computational Pathology Biomarker Discovery

arXiv Feb 1, 2026
Despite significant progress in computational pathology, many AI models remain black-box and difficult to interpret, posing a major barrier to clinical adoption due to limited transparency and explainability. This has motivated continued interest in ... read more 
Machine Learning
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