Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
Different medical imaging modalities capture diagnostic information at varying spatial resolutions, from coarse global patterns to fine-grained localized structures. However, most existing vision-language frameworks in the medical domain apply a uniform strategy for local feature extraction, overlooking the modality-specific demands. In this work, we present MedMoE, a modular and extensible visi...
The integration of deep learning-based glaucoma detection with large language models (LLMs) presents an automated strategy to mitigate ophthalmologist shortages and improve clinical reporting efficiency. However, applying general LLMs to medical imaging remains challenging due to hallucinations, limited interpretability, and insufficient domain-specific medical knowledge, which can potentially r...
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in understanding common visual elements, largely due to their lar...
Universal medical image segmentation using the Segment Anything Model (SAM) remains challenging due to its limited adaptability to medical domains. ...
Medical professionals, especially those in training, often depend on visual reference materials to support an accurate diagnosis and develop pattern...
Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as we...
The application of AI for predicting critical heart failure endpoints using echocardiography is a promising avenue to improve patient care and treatme...
Objective: While recent advances in text-conditioned generative models have enabled the synthesis of realistic medical images, progress has been lar...
Purpose: We investigated the utilization of privacy-preserving, locally-deployed, open-source Large Language Models (LLMs) to extract diagnostic inf...
Recent vision-language foundation models deliver state-of-the-art results on natural image classification but falter on medical images due to pronou...
Recent advances in reinforcement learning with verifiable, rule-based rewards have greatly enhanced the reasoning capabilities and out-of-distributi...
Peritoneal metastasis is a key factor in the poor prognosis of advanced gastrointestinal cancer patients. Traditional radiological diagnostic faces ch...
Medical image segmentation is vital for clinical diagnosis, yet current deep learning methods often demand extensive expert effort, i.e., either thr...
Medical anomaly detection (AD) is crucial for early clinical intervention, yet it faces challenges due to limited access to high-quality medical ima...
Advanced clinical practitioners (ACPs) play an essential role in dermatological care but often encounter challenges due to limited training in dermato...
Foundation models (FMs) such as CLIP and SAM have recently shown great promise in image segmentation tasks, yet their adaptation to 3D medical imagi...
Medical Vision-Language Models (MVLMs) have achieved par excellence generalization in medical image analysis, yet their performance under noisy, cor...
Artificial intelligence (AI) incorporation into healthcare has proven revolutionary, especially in radiotherapy, where accuracy is critical. The purpo...
Visual grounding is essential for precise perception and reasoning in multimodal large language models (MLLMs), especially in medical imaging domain...
Esophageal cancer (EC), a common malignant tumor of the digestive tract, requires early diagnosis and timely treatment to improve patient prognosis. A...