SUMMARY A deep learning tool that quickly and accurately estimates prostate tissue composition from MRI scans, combining physical modeling and AI to provide robust, non-invasive cancer diagnostics without needing large, labeled datasets The Unmet Need: Advanced imaging techniques that can rapidly and… Read More
SUMMARY A method uses AI and clustering to remove lesions from breast MRI images, reconstructing clearer projection images for more accurate assessment of tissue enhancement and improved breast cancer risk evaluation. The Unmet Need: Automated techniques to accurately distinguish and isolate lesions… Read More
SUMMARY Novel machine learning-enable platform to guide in vitro experimental screening and discovery of small molecule immunomodulators to improve immune responses The unmet need: Platform to resolve unregulated immune potentiator enhancement and suppression of inflammatory and stimulatory responses The innate immune response… Read More
SUMMARY Despite the many advances in the field of resuscitation post cardiac arrest, hypoxic-ischemic brain injury (HIBI) leaves many survivors with severe neurological disability. Patients who remain comatose at 24–72 h or more after resuscitation undergo neuroprognostication, aimed to detect signs of HIBI and predict… Read More
SUMMARY Deep learning tools are becoming increasingly popular for histologic analyses and have been used to predict tumor biomarker status, clinical variables, tumor subtypes, and mutation status in a variety of cancers. Accurate prediction of recurrence risk for post-operative cancer recurrence is of significant clinical interest as it can be… Read More
SUMMARY With the explosive growth of high-dimensional data, approximate methods emerge as promising solutions for searching for similar data pairs. Quantization methods have gained prominence due to their low storage costs and fast query responses. These methods decompose data dimensions into subspaces and their performance critically depends on maintaining effective… Read More
SUMMARY Atom interferometry is used in sensors designed to measure gravitational field, acceleration, and angular momentum and is of increasing importance to the emerging field of quantum communications, which shows great promise for fast, secure, and precise relay of information. Interpreting the results of an interferometer requires precise calibration… Read More
SUMMARY Multi-task learning is a subfield of machine learning in which multiple tasks are simultaneously learned by a shared model. Biologically-inspired recurrent neural networks (RNNs), trained using backpropagation, can learn an impressive array of complex tasks, and offer the opportunity for detailed examination of network activity and circuit structure. Read More
SUMMARY There have been significant research efforts to develop machine learning and predictive analytics based tools to predict viral evolution which remains the main obstacle in the early detection of drug-resistant strains and facilitate the design of more efficient antiviral treatments. Machine learning and advanced algorithms have… Read More
SUMMARY Catastrophic forgetting or interference is the inability of an artificial neural network (ANN) to learn multiple sequential tasks without a degradation in the accuracy of a previously learned task. When an ANN learns a new task, it disrupts connection weights that were important for solving a previous task. Current… Read More