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Keep up with the latest news from the NCI Center for Biomedical Informatics and Information Technology (CBIIT) and the data science communities.

Image of a human head. Three areas in the brain are colored red to signify brain tumors. The tumors are set against a background of lines denoting artificial intelligence.

NCI Funding Helps Bring New AI-Aided Device for Glioblastoma to Market

Thanks to funding from NCI’s Small Business Innovation Research program, a new tool recently received FDA clearance. See how this cyber device could help you with artificial intelligence (AI)-guided glioblastoma segmentation.

GammaGateR helps identify biomarkers in multiplexed immunofluorescence images. Image shows markers in red and blue within a single slide sample.

GammaGateR Helps Decipher Imaging Data

Looking for an easier way to sort and quantify key cellular information from immunofluorescent images? NCI-funded researchers have a new semi-automated tool, called “GammaGateR,” that may help.

Multimodal image of cancer tumor cells shows how the Orion tool is able to combine two types of images—hematoxylin and eosin and immunofluorescence—in a single slide.

NCI-Funded Tool Combines Imaging Modalities to Aid Pathologists and Machine Learning (ML)

An NCI-funded technology blends specific molecular markers along with traditional morphological features in the same cells and in one digital slide. This tool could someday help pathologists and ML models better predict cancer treatment response and outcomes.

A hand is holding a tablet. Illuminating from the table is the image of a clock. This abstract image conveys how technology is helping monitor disease and predict the course of cancer over time.

AI, Genomics, and Clinical Data Help Track Cancer’s Evolution Over Time

An international team of researchers combined genomics, biopsy results, and artificial intelligence (AI) to track prostate cancer over time. Learn more about these “evolvability” metrics and how they could someday help predict cancer re-occurrence.

MRI image of an enlarged prostate.

NCI Uses AI to Take the Guesswork Out of Assessing Prostate Cancer Images

NCI researchers are using artificial intelligence (AI) to uncover additional information from medical images. This helps them not only to diagnose cancer but also to predict how it will progress and if it will re-occur.

Artist illustration of lungs set against a background of a stained pathology slide.

NCI-Funded Researchers Develop a New Model for Interpreting Pathology

Interpreting whole slide images can be a labor intensive and difficult task. A recent article describes a new approach that helps classify cancer and predict how it will progress.

A person wearing a stethoscope sits at a lap top computer. Above the keyboard are a number of small icons and dotted lines, signifying artificial intelligence. The letters "AI" appear just above the keyboard.

Artificial Intelligence Aids in Predicting Prostate Cancer

See how NCI researchers are using artificial intelligence to develop tools that may someday help oncologists make more informed decisions when caring for people with prostate cancer.

An illustrated colon is shown, cradled in the outstretched hands of a doctor wearing a white lab coat.

Machine Learning Helps Refine a New Approach for Early Colorectal Cancer Screening

NCI-funded researchers are using machine learning to help identify an early-warning screening approach for colorectal cancer.

A stick drawing of a person is shown inside an imaging machine. Circles with dots and dashes encircle the image, designating a high-tech approach to cancer imaging.

See What’s New in NCI’s Imaging Data Commons (IDC)

NCI’s Imaging Data Commons has new data available, and, thanks to AI and cloud computing, it’s easier than ever before to work with this imaging data.

open laptop with a picture of a brain and data charts on the screen. There are also other data themed icons including a data cloud, gears, and a magnifying glass.

Participate in the NCI Medical Image De-Identification Benchmark Challenge at MICCAI 2024

Explore this challenge if you’re a software tool developer interested in assessing how well your tool de-identifies medical images.

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