This study introduces MedicalRec, a medical recommender system for image classification, designed to optimize model selection without the need for extensive retraining. It addresses the computational and energy challenges of model identification by leveraging a publicly available dataset, MedicalRec-Bench, compiled from 3,000 articles and over 5,000 tested model records.
This research paper explores the automatic extraction of data from brain MRI reports using the open-weight large language model LLaMA 3.1. It evaluates the LLM's performance in analyzing Dutch neuroradiology reports, demonstrating high zero-shot performance.
This is a discussion thread to consolidate information sharing regarding early acceptances, rebuttals, and rejections for the MICCAI 2026 conference.
This content discusses the development and validation of deep learning algorithms designed to detect critical findings in head CT scans. The research explores the application of artificial intelligence to improve detection in medical diagnostics.
ChatCAD is an interactive computer-aided diagnosis system that leverages Large Language Models to analyze medical images. It aims to enhance the accuracy and efficiency of medical diagnosis through artificial intelligence.
This content presents a methodology for diagnosing Alzheimer's Disease using a deeply supervised and adaptable 3D Convolutional Network. The research explores the use of advanced deep learning to improve accuracy in medical image diagnostics.
This research develops an interpretable AI framework combining deep learning-based MRI Osteoarthritis Knee Score (MOAKS) prediction with interpretable statistical modeling to study structure-pain relationships at scale using data from the Osteoarthritis Initiative (OAI). It utilizes deep learning for MOAKS feature prediction from MRIs with uncertainty quantification, followed by a longitudinal latent class mixed model to examine associations between structural abnormalities and knee pain.
This research paper proposes a method for detecting retinal blood vessels in fundus images using an ensemble of Deep Convolutional Neural Networks. The approach aims to improve diagnostic accuracy through advanced image analysis.
The author built and launched "PneumoScan AI," a deep learning model that detects pneumonia from chest X-rays with over 90% accuracy, aiming to address slow diagnosis in low-resource areas. This article details the development process, including the use of a Kaggle dataset and the discovery of its imbalance.
This research presents an artifact-based agent framework to enhance medical image processing, focusing on adaptability and reproducibility. It introduces a semantic layer and an artifact contract to enable structured workflow interrogation and goal-conditioned configuration based on dataset-specific conditions.
The paper introduces Dual-Spectral Flow Matching (DSFM), a novel fMRI generative framework that cascades dual frequency representation of BOLD signals with spectral flow matching. This method aims to synthesize high-fidelity fMRI time series to overcome limitations in data availability for brain disorder identification, by replicating complex spatiotemporal dynamics.
OralAgent is presented as the first dental-specialized AI agent that unifies multimodal reasoning, tool-based decision-making, and knowledge-grounded retrieval for interactive dental image analysis. It integrates 22 visual analysis tools and 368 dental textbooks, alongside a new large-scale bilingual textual resource called OralCorpus.
This paper proposes an innovative method to enhance pancreas segmentation in CT and MRI images. It utilizes recurrent neural contextual learning and a direct loss function to optimize accuracy.
This content introduces a novel approach to adaptive ultrasound imaging leveraging a Physics-Informed AI model named NV-Raw2Insights-US. It highlights the application of advanced AI techniques to enhance adaptability in medical ultrasound diagnostics.
O MedGemma 1.5 4B é um novo modelo que expande as capacidades do MedGemma 1, integrando análise de imagens médicas de alta dimensão (CT/MRI, histopatologia), localização anatômica e compreensão de documentos médicos. Ele demonstra ganhos significativos em precisão de classificação de condições em MRI e CT, e um aumento de 47% no macro F1 para imagens de patologia de lâmina inteira.
This article provides a friendly introduction to Foundation Models applied in Computational Pathology. It explores how artificial intelligence can analyze microscope slides to extract valuable information.
The text describes Optical Coherence Tomography (OCT), an eye imaging technology invented by David Huang. This noninvasive technique is now used in 40 million procedures per year to image the inside of the eye.