medical-imaging

Nature Biomedical Engineering, Published online: 24 April 2026; doi:10.1038/s41551-026-01662-2 The OCTCube models integrate optical coherence tomography and other imaging modalities to achieve state-of-the-art performance in predicting retinal diseases with strong generalizability across cohorts, devices and modalities.

Accurate detection of pediatric fractures in radiographs remains challenging due to subtle visual cues and the high prevalence of false-positive detections produced by automated systems. To address this limitation, we propose a lightweight region-of-interest (Region of Interest) adjudication framework that operates as a second-stage verification module to refine detector-generated candidates. The…

Journal of Computer Science, Published online: 18 April 2026; doi:10.3844/jcssp.2026.1254.1278 Image denoising is a vital process in medical imaging that involves removing noise or distortions introduced during image acquisition. Random noise can degrade image quality and reduce contrast, makin...

BackgroundBias in medical image segmentation can lead to unequal performance across demographic subgroups, raising concerns about fairness and reliability in clinical AI systems. While deep learning models have achieved high segmentation accuracy, ensuring equitable performance across race and gender remains a significant challenge, particularly in privacy-sensitive healthcare environments.Method…

AI Diagnoses Knees, Drugs, Configs, and License‑Buying Agents Artificial intelligence is moving beyond research labs into everyday tools. From medical imaging to drug pipelines, from config automation to autonomous agents, the week shows how AI is reshaping both health and development workflows. The momentum spans clinical trials, financing rounds, and open‑source tooling, signaling a broader shi…

Nature Biomedical Engineering, Published online: 26 March 2026; doi:10.1038/s41551-026-01638-2 A video-based deep-learning system was trained to understand the spectrum of human cardiovascular disease by the self-supervised method of contrastive learning, using pairs of cardiac MRI scans and their corresponding text reports that are generated as part of routine clinical practice.

Nature Biomedical Engineering, Published online: 07 April 2026; doi:10.1038/s41551-026-01639-1 BUSGen is a foundation generative model designed for analysing breast ultrasound images that supports diverse tasks and improves breast cancer screening, diagnosis and prognosis.

A new hybrid imaging system brings optical contrast into ultrasound to visualize the human body in 3D.

Artificial intelligence has shown strong promise in diabetic retinopathy screening, with research models achieving impressive accuracy on curated retinal image datasets. However, the path from a published model to a system that can operate reliably in real clinical environments is far more complex.

After taking a two and a half year break since its last acquisition, Philips acquired SpectraWAVE for its HyperVue and X1-FFR technologies in a bid to extend its intravascular imaging lead. At the center of this acquisition, Philips’ Azurion is an image-guided therapy platform that already serves 7.6M patients annually. It combines coronary imaging and […] The post Philips Acquires SpectraWAVE, M…

Duke Researchers Selected to National Academy of Inventors Two Duke innovators will become NAI Fellows: Aravind Asokan, for gene therapy delivery work, and Adam Wax, for medical imaging and optics advances Wax is an expert in medical imaging and optical technologies. His innovations are being used to improve medical imaging systems like endoscopy for esophageal cancer and optical coherence tomogr…

Northwestern Medicine scientists have developed an AI tool that not only matches doctors in accurately outlining lung tumors on CT scans but can also identify areas that some doctors may miss, according to a new study. The post AI Matches Doctors in Mapping Lung Tumors for Radiation Therapy appeared first on News Center .

This paper presents SimNorth, an unsupervised deep learning method for organizing non-standard fetal ultrasound images. By learning feature embeddings using a novel contrastive loss and clustering similar anatomical structures, SimNorth outperforms existing methods like Autoencoders, MoCo, and SimCLR in identifying meaningful image groups. The post SimNorth: A novel contrastive learning approach …

Breast cancer and chronic kidney disease are very common and thus, a large population could benefit from the clinical implementation of super-resolution ultrasound imaging. The post Super-resolution ultrasound to assess kidney failure and breast cancer first appeared on Acoustics.org .

Medical Image Segmentation is one of the most important applications of Deep Learning in healthcare. Yet, most people only know 2D check x-ray segmentation. What about the 3D Scans? What about Foundation Models? In this article, we're going to dive into it!

3.6 billion. That’s about how many medical imaging tests are performed annually worldwide to diagnose, monitor and treat various conditions. Speeding up the processing and evaluation of all these X-rays, CT scans, MRIs and ultrasounds is essential to helping doctors manage their workloads and to improving health outcomes. That’s why NVIDIA introduced MONAI, which serves […]

BiomedParse reimagines medical image analysis, integrating advanced AI to capture complex insights across imaging types—a step forward for diagnostics and precision medicine. The post BiomedParse: A foundation model for smarter, all-in-one biomedical image analysis appeared first on Microsoft Research .

X-ray Imaging: Penetrating the Body’s Interior In the realm of medical imaging, X-ray technology plays a pivotal role in peering inside the body’s interior. Join me in exploring the physics behind X-ray imaging, where high-energy beams pass through tissues, unveiling a detailed look at the skeletal structure and detecting abnormalities that remain hidden to the […] The post The Role of Physics in…