Digital Imaging and Communications in Medicine
PulseAugur coverage of Digital Imaging and Communications in Medicine — every cluster mentioning Digital Imaging and Communications in Medicine across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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Medical AI poses new privacy risks beyond traditional health IT
Medical AI systems, while advancing diagnostics and patient care, introduce novel privacy risks that traditional health IT safeguards do not address. These models can inadvertently reveal sensitive patient data through …
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Asus ProArt PA27USD OLED Monitor Blends Pro Color Accuracy with Gaming Speed
The Asus ProArt PA27USD is a 27-inch QD-OLED monitor that excels in both professional color-critical work and high-speed gaming. It boasts near-perfect color accuracy out of the box, a built-in calibrator, and a wide ga…
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Medical Imaging AI Vulnerable to Unmonitored Acquisition State Changes
A new research paper highlights a critical, unmonitored variable in medical imaging AI: the acquisition state. The study demonstrates that changes in reconstruction kernels, even when patient and acquisition parameters …
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New architecture links radiology report findings to evidence
Researchers have proposed a new reference architecture for structured radiology reporting that links evidence directly to report content. This human-supervised system aims to extract and organize structured information,…
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New AI framework integrates image and metadata for DICOM classification
Researchers have developed a new multimodal framework for classifying DICOM image series, integrating both image content and acquisition metadata. This approach uses a bi-directional cross-modal attention mechanism and …
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Jneopallium project integrates diverse protocols for model building
The Jneopallium project has released updates demonstrating its capability to build models using various data protocols and standards. These updates showcase integration with OpenTelemetry, HL7 FHIR, MQTT with Sparkplug …
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AI analyzes compressed CT scans efficiently with new FAST and SFP techniques
Researchers have developed a new framework called CT-Lite to enable AI analysis of compressed chest CT scans, addressing the computational burden of medical imaging data. The system utilizes Feature Attention Style Tran…