Artificial Intelligence Medical Compendium

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

Showing 58,251 to 58,260 of 227,634 articles

MV-SAM: Multi-view Promptable Segmentation using Pointmap Guidance

arXiv Jan 25, 2026
Promptable segmentation has emerged as a powerful paradigm in computer vision, enabling users to guide models in parsing complex scenes with prompts such as clicks, boxes, or textual cues. Recent advances, exemplified by the Segment Anything Model (S... read more 
Computer Vision and Pattern Recognition

Masked Depth Modeling for Spatial Perception

arXiv Jan 25, 2026
Spatial visual perception is a fundamental requirement in physical-world applications like autonomous driving and robotic manipulation, driven by the need to interact with 3D environments. Capturing pixel-aligned metric depth using RGB-D cameras woul... read more 
Computer Vision and Pattern Recognition cs.RO

UniPACT: A Multimodal Framework for Prognostic Question Answering on Raw ECG and Structured EHR

arXiv Jan 25, 2026
Accurate clinical prognosis requires synthesizing structured Electronic Health Records (EHRs) with real-time physiological signals like the Electrocardiogram (ECG). Large Language Models (LLMs) offer a powerful reasoning engine for this task but stru... read more 
Machine Learning

Benchmarking Direct Preference Optimization for Medical Large Vision-Language Models

arXiv Jan 25, 2026
Large Vision-Language Models (LVLMs) hold significant promise for medical applications, yet their deployment is often constrained by insufficient alignment and reliability. While Direct Preference Optimization (DPO) has emerged as a potent framework ... read more 
Computer Vision and Pattern Recognition cs.CL

RemEdit: Efficient Diffusion Editing with Riemannian Geometry

arXiv Jan 25, 2026
Controllable image generation is fundamental to the success of modern generative AI, yet it faces a critical trade-off between semantic fidelity and inference speed. The RemEdit diffusion-based framework addresses this trade-off with two synergistic ... read more 
Computer Vision and Pattern Recognition cs.MM

From Specialist to Generalist: Unlocking SAM's Learning Potential on Unlabeled Medical Images

arXiv Jan 25, 2026
Foundation models like the Segment Anything Model (SAM) show strong generalization, yet adapting them to medical images remains difficult due to domain shift, scarce labels, and the inability of Parameter-Efficient Fine-Tuning (PEFT) to exploit unlab... read more 
Computer Vision and Pattern Recognition cs.AI

DTC: A Deformable Transposed Convolution Module for Medical Image Segmentation

arXiv Jan 25, 2026
In medical image segmentation, particularly in UNet-like architectures, upsampling is primarily used to transform smaller feature maps into larger ones, enabling feature fusion between encoder and decoder features and supporting multi-scale predictio... read more 
Computer Vision and Pattern Recognition

Boosting methods for interval-censored data with regression and classification

arXiv Jan 25, 2026
Boosting has garnered significant interest across both machine learning and statistical communities. Traditional boosting algorithms, designed for fully observed random samples, often struggle with real-world problems, particularly with interval-cens... read more 
Machine Learning cs.LG

Domain-Expert-Guided Hybrid Mixture-of-Experts for Medical AI: Integrating Data-Driven Learning with Clinical Priors

arXiv Jan 25, 2026
Mixture-of-Experts (MoE) models increase representational capacity with modest computational cost, but their effectiveness in specialized domains such as medicine is limited by small datasets. In contrast, clinical practice offers rich expert knowled... read more 
Computer Vision and Pattern Recognition

Federated learning for unpaired multimodal data through a homogeneous transformer model

arXiv Jan 25, 2026
Training of multimodal foundation models is currently restricted to centralized data centers containing massive, aligned datasets (e.g., image-text pairs). However, in realistic federated environments, data is often unpaired and fragmented across dis... read more 
Machine Learning
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