Search Technologies

The National Cancer Institute (NCI) seeks parties interested in licensing software for the targeted delivery of anti-cancer agents in solid tumors.

The Office of the Director, National Cancer Institute is seeking statements of capability or interest from parties interested in collaborative research (using the Cooperative Research and Development Agreement (CRADA) or Material Transfer Agreement (MTA) to further develop, evaluate, or commercialize the software for accurate segmentation of cell nuclei and FISH signals in tissue sections. Collaborators working in the field of quantitative and automated pathology may be interested.

Researchers from NCI and Rudgers University developed  methods of detecting abnormal cells in a sample using the spatial position of one or more genes within the nucleus of a cell, as well as a kit for detecting abnormal cells using such methods. The invention also provides methods of identifying gene markers for abnormal cells using the spatial position of one or more genes within the nucleus of a cell. The National Cancer Institute seeks parties interested in collaborative research to co-develop diagnostic methods for detection of cancer using spatial genome organization.

The National Institute on Drug Abuse (NIDA) seeks licensing and/or co-development research collaborations for use of software for substance use disorders, behavior modification, and cancer patient care and pain management, etc. NIDA has developed software that permits real-time communication of patient-reported data and associated geolocation data. The software can be used in patient treatment or as a research tool for evaluating effectiveness of treatments.

Hospital Picture Archiving and Communication Systems (PACS) contain vast amounts of underutilized informatics about disease conditions. As computer image processing and systems advance, PACS informatics may form the foundation for precision automated computer-aided diagnostics for a wide range of disease conditions. Development of such systems may improve diagnostic accuracy and better inform treatment, but creating systems and algorithms capable of “learning” to recognize and locate the image patterns of disease and associated labels is a difficult problem. Researchers at the National Institutes of Health Clinical Center (NIHCC) have developed a technology that applies deep learning to PACS images to produce a database where certain disease features are identified and spatially located. Researchers at the NIHCC seek licensing of the PACS.

The National Institutes of Health - Clinical Center (NIH-CC) seeks to license and/or co-develop methods of reading chest x-rays using a deep learning models to detect a disease and describe its contents.

Researchers at the National Institute on Drug Abuse (NIDA) seek licensing or co-development of a Human Research Information System (HuRIS) software that automates all major functions of a clinical-research entity. The system is designed for commercial healthcare providers, community treatment centers, and clinical research facilities.

Scientists at The National Cancer Institute (NCI) and The National Institute of Neurological Disorders and Stroke (NINDS) have invented a method of imaging glucose metabolism in vivo using MRI chemical shift imaging (CSI) experiments that relies on a simple, but robust and efficient, post-processing procedure by the higher dimensional analog of singular value decomposition, tensor decomposition. This new technology is denoising software for MRIs that significantly improves the measurement of low-intensity signals without the need for dynamic nuclear polarization (DNP). The scientists seek research co-development partners and/or licensees for their invention.

Computer automated segmentation of high variability organs and disease features in medical images is uniquely difficult. The application of deep learning and specialized neural networks may allow for automation of such interpretation tasks that are currently only performed by trained physicians. Computer automation may improve image analysis capabilities and lead to better diagnostics, disease monitoring, and surgical planning for many diseases. To help solve this challenge, researchers at the National Institutes of Health Clinical Center (NIHCC) have developed a technology that trains a computer to read and segment certain highly variable image features.