Artificial Intelligence Medical Compendium

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

Showing 34,051 to 34,060 of 221,510 articles

Finding Holes: Pathologist-Level Performance Using AI for Cribriform Morphology Detection in Prostate Cancer.

European urology open science Apr 7, 2026
BACKGROUND: Cribriform morphology in prostate cancer is a histological feature that indicates poor prognosis and contraindicates active surveillance. However, it remains underreported and subject to significant interobserver variability among patholo... read more 

Deep learning analysis of sugar beet (Beta vulgaris ssp vulgaris) protease inhibitors interacting with identified trypsins from its primary pathogen, the sugar beet root maggot Tetanops myopaeformis.

Data in brief Apr 7, 2026
Beta vulgaris ssp. vulgaris (sugar beet) is one of two plants globally from which sugar is widely produced, accounting for 55% of U.S. sugar $1B (U.S.) and 35% of global raw sugar $4.6B), annually. Its top pathogen, Tetanops myopaeformis, is capable ... read more 

Gym-Anything: Turn any Software into an Agent Environment

arXiv Apr 7, 2026
Computer-use agents hold the promise of assisting in a wide range of digital economic activities. However, current research has largely focused on short-horizon tasks over a limited set of software with limited economic value, such as basic e-commerc... read more 
Machine Learning cs.AI

PoM: A Linear-Time Replacement for Attention with the Polynomial Mixer

arXiv Apr 7, 2026
This paper introduces the Polynomial Mixer (PoM), a novel token mixing mechanism with linear complexity that serves as a drop-in replacement for self-attention. PoM aggregates input tokens into a compact representation through a learned polynomial fu... read more 
Computer Vision and Pattern Recognition cs.AI

The Character Error Vector: Decomposable errors for page-level OCR evaluation

arXiv Apr 7, 2026
The Character Error Rate (CER) is a key metric for evaluating the quality of Optical Character Recognition (OCR). However, this metric assumes that text has been perfectly parsed, which is often not the case. Under page-parsing errors, CER becomes un... read more 
Computer Vision and Pattern Recognition cs.LG

DiffHDR: Re-Exposing LDR Videos with Video Diffusion Models

arXiv Apr 7, 2026
Most digital videos are stored in 8-bit low dynamic range (LDR) formats, where much of the original high dynamic range (HDR) scene radiance is lost due to saturation and quantization. This loss of highlight and shadow detail precludes mapping accurat... read more 
Computer Vision and Pattern Recognition cs.AI cs.GR

HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models

arXiv Apr 7, 2026
Large vision-language models can produce object hallucinations in image descriptions, highlighting the need for effective detection and mitigation strategies. Prior work commonly relies on the model's attention weights on visual tokens as a detection... read more 
Computer Vision and Pattern Recognition cs.LG

Topological Characterization of Churn Flow and Unsupervised Correction to the Wu Flow-Regime Map in Small-Diameter Vertical Pipes

arXiv Apr 7, 2026
Churn flow-the chaotic, oscillatory regime in vertical two-phase flow-has lacked a quantitative mathematical definition for over $40$ years. We introduce the first topology-based characterization using Euler Characteristic Surfaces (ECS). We formulat... read more 
Machine Learning math.AT

Action Images: End-to-End Policy Learning via Multiview Video Generation

arXiv Apr 7, 2026
World action models (WAMs) have emerged as a promising direction for robot policy learning, as they can leverage powerful video backbones to model the future states. However, existing approaches often rely on separate action modules, or use action re... read more 
Computer Vision and Pattern Recognition cs.RO

MO-RiskVAE: A Multi-Omics Variational Autoencoder for Survival Risk Modeling in Multiple MyelomaMO-RiskVAE

arXiv Apr 7, 2026
Multimodal variational autoencoders (VAEs) have emerged as a powerful framework for survival risk modeling in multiple myeloma by integrating heterogeneous omics and clinical data. However, when trained under survival supervision, standard latent reg... read more 
Machine Learning cs.AI
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