Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
May 23, 2014
Modern medical information retrieval systems are paramount to manage the insurmountable quantities of clinical data. These systems empower health care experts in the diagnosis of patients and play an important role in the clinical decision process. H...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 24, 2014
Retrieval systems can supply similar cases with a proven diagnosis to a new example case under observation to help clinicians during their work. The ImageCLEFmed evaluation campaign proposes a framework where research groups can compare case-based re...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 27, 2014
Medical image retrieval and classification have been extremely active research topics over the past 15 years. Within the ImageCLEF benchmark in medical image retrieval and classification, a standard test bed was created that allows researchers to com...
PURPOSE: A critical limitation to deployment and utilization of Artificial Intelligence (AI) algorithms in radiology practice is the actual integration of algorithms directly into the clinical Picture Archiving and Communications Systems (PACS). Here...
Radiographics : a review publication of the Radiological Society of North America, Inc
Sep 1, 2025
In radiology practice, medical images are described and interpreted by radiologists in text reports. Recent technical developments enabling deep learning models to connect images and text may facilitate the radiologic workflow. These developments inc...
BACKGROUND: Radiology reports are essential in medical imaging, providing critical insights for diagnosis, treatment, and patient management by bridging the gap between radiologists and referring physicians. However, the manual generation of radiolog...
Studies in health technology and informatics
Aug 7, 2025
This study proposes RRG-LLM, a model designed to enhance RRG by effectively learning medical domain with minimal computational resources. Initially, LLM is finetuned by LoRA, enabling efficient adaptation to the medical domain. Subsequently, only the...
OBJECTIVE: To evaluate feasibility of large language models (LLMs) to convert radiologist-generated report summaries into personalized report templates, and assess its impact on scan reporting time and quality.
The radiology report is essential for doctors' diagnosis and treatment. The automatic generation of radiology reports can assist doctors in diagnosis and treatment, thereby reducing their workload. Some existing studies consider the entire radiologic...
Automatic radiology report generation can alleviate the workload for physicians and minimize regional disparities in medical resources, therefore becoming an important topic in the medical image analysis field. It is a challenging task, as the comput...
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