Artificial Intelligence Medical Compendium

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

Showing 61,771 to 61,780 of 229,091 articles

MMedExpert-R1: Strengthening Multimodal Medical Reasoning via Domain-Specific Adaptation and Clinical Guideline Reinforcement

arXiv
Medical Vision-Language Models (MedVLMs) excel at perception tasks but struggle with complex clinical reasoning required in real-world scenarios. While reinforcement learning (RL) has been explored to enhance reasoning capabilities, existing approach... read more 

PatientVLM Meets DocVLM: Pre-Consultation Dialogue Between Vision-Language Models for Efficient Diagnosis

arXiv
Traditionally, AI research in medical diagnosis has largely centered on image analysis. While this has led to notable advancements, the absence of patient-reported symptoms continues to hinder diagnostic accuracy. To address this, we propose a Pre-Co... read more 

Sparse Data Tree Canopy Segmentation: Fine-Tuning Leading Pretrained Models on Only 150 Images

arXiv
Tree canopy detection from aerial imagery is an important task for environmental monitoring, urban planning, and ecosystem analysis. Simulating real-life data annotation scarcity, the Solafune Tree Canopy Detection competition provides a small and im... read more 

MATEX: Multi-scale Attention and Text-guided Explainability of Medical Vision-Language Models

arXiv
We introduce MATEX (Multi-scale Attention and Text-guided Explainability), a novel framework that advances interpretability in medical vision-language models by incorporating anatomically informed spatial reasoning. MATEX synergistically combines mul... read more 

Self-learned representation-guided latent diffusion model for breast cancer classification in deep ultraviolet whole surface images

arXiv
Breast-Conserving Surgery (BCS) requires precise intraoperative margin assessment to preserve healthy tissue. Deep Ultraviolet Fluorescence Scanning Microscopy (DUV-FSM) offers rapid, high-resolution surface imaging for this purpose; however, the sca... read more 

Classification of Chest XRay Diseases through image processing and analysis techniques

arXiv
Multi-Classification Chest X-Ray Images are one of the most prevalent forms of radiological examination used for diagnosing thoracic diseases. In this study, we offer a concise overview of several methods employed for tackling this task, including De... read more 

Graph Neural Network Reveals the Local Cortical Morphology of Brain Aging in Normal Cognition and Alzheimers Disease

arXiv
Estimating brain age (BA) from T1-weighted magnetic resonance images (MRIs) provides a useful approach to map the anatomic features of brain senescence. Whereas global BA (GBA) summarizes overall brain health, local BA (LBA) can reveal spatially loca... read more 

Integrating multiomics data using a correlation based graph attention network for subtype classification in lower grade glioma.

Discover oncology
Accurate classification of cancer subtypes is crucial for personalised therapies and targeted interventions. In this study, we propose BioGAT-LGG, a deep learning framework that integrates multi-omics data, including mRNA, miRNA, and DNA methylation,... read more 

External validation of the IHXGboost-P model to predict incisional hernia after midline laparotomy.

Hernia : the journal of hernias and abdominal wall surgery
BACKGROUND: Incisional hernia (IH) is a significant complication that occurs after midline laparotomy and is associated with high morbidity and economic impacts. A fundamental goal of preventing IH is to determine which patients are considered low- o... read more 

Single-cell profiling of immunogenic cell death in melanoma reveals prognostic signatures and therapeutic targets.

Discover oncology
BACKGROUND: Cutaneous melanoma is one of the most aggressive forms of skin cancer, marked by rapid progression, early metastasis, and high resistance to conventional therapies. Although targeted therapies and immune checkpoint blockade have improved ... read more