Artificial Intelligence Medical Compendium

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

Showing 28,051 to 28,060 of 219,064 articles

PanGuide3D: Cohort-Robust Pancreas Tumor Segmentation via Probabilistic Pancreas Conditioning and a Transformer Bottleneck

arXiv Apr 22, 2026
Pancreatic tumor segmentation in contrast-enhanced computed tomography (CT) is clinically important yet technically challenging: lesions are often small, heterogeneous, and easily confused with surrounding soft tissue, and models that perform well on... read more 
Quantitative Methods cs.CV cs.LG

Thinking Like a Botanist: Challenging Multimodal Language Models with Intent-Driven Chain-of-Inquiry

arXiv Apr 22, 2026
Vision evaluations are typically done through multi-step processes. In most contemporary fields, experts analyze images using structured, evidence-based adaptive questioning. In plant pathology, botanists inspect leaf images, identify visual cues, in... read more 
Computer Vision and Pattern Recognition cs.AI cs.CL

Droplet-LNO: Physics-Informed Laplace Neural Operators for Accurate Prediction of Droplet Spreading Dynamics on Complex Surfaces

arXiv Apr 22, 2026
Spreading of liquid droplets on solid substrates constitutes a classic multiphysics problem with widespread applications ranging from inkjet printing, spray cooling, to biomedical microfluidic systems. Yet, accurate computational fluid dynamic (CFD) ... read more 
Machine Learning

Linear Image Generation by Synthesizing Exposure Brackets

arXiv Apr 22, 2026
The life of a photo begins with photons striking the sensor, whose signals are passed through a sophisticated image signal processing (ISP) pipeline to produce a display-referred image. However, such images are no longer faithful to the incident ligh... read more 
Computer Vision and Pattern Recognition

Unlocking Multi-Spectral Data for Multi-Modal Models with Guided Inputs and Chain-of-Thought Reasoning

arXiv Apr 22, 2026
Multi-spectral imagery is a valuable input signal for Remote Sensing applications, such as land-use and land-cover classification and environmental monitoring. However, generalist Large Multi-modal Models (LMMs) are typically trained on RGB images, l... read more 
Computer Vision and Pattern Recognition

Projected Gradient Unlearning for Text-to-Image Diffusion Models: Defending Against Concept Revival Attacks

arXiv Apr 22, 2026
Machine unlearning for text-to-image diffusion models aims to selectively remove undesirable concepts from pre-trained models without costly retraining. Current unlearning methods share a common weakness: erased concepts return when the model is fine... read more 
Computer Vision and Pattern Recognition

JEPAMatch: Geometric Representation Shaping for Semi-Supervised Learning

arXiv Apr 22, 2026
Semi-supervised learning has emerged as a powerful paradigm for leveraging large amounts of unlabeled data to improve the performance of machine learning models when labeled data are scarce. Among existing approaches, methods derived from FixMatch ha... read more 
Machine Learning

StyleVAR: Controllable Image Style Transfer via Visual Autoregressive Modeling

arXiv Apr 22, 2026
We build on the Visual Autoregressive Modeling (VAR) framework and formulate style transfer as conditional discrete sequence modeling in a learned latent space. Images are decomposed into multi-scale representations and tokenized into discrete codes ... read more 
Computer Vision and Pattern Recognition cs.AI

Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images

arXiv Apr 22, 2026
Accurate diagnosis of pediatric brain tumors, starting with histopathology, presents unique challenges for deep learning, including severe data scarcity, class imbalance, and fine-grained morphologic overlap across diagnostically distinct subtypes. W... read more 
Computer Vision and Pattern Recognition

Optimizing Diffusion Priors with a Single Observation

arXiv Apr 22, 2026
While diffusion priors generate high-quality posterior samples across many inverse problems, they are often trained on limited training sets or purely simulated data, thus inheriting the errors and biases of these underlying sources. Current approach... read more 
Computer Vision and Pattern Recognition cs.LG stat.ME
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