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Full Spectrum Computer Vision for Photon Counting CT (Case No. 2024-058)

Summary: Researchers in the Department of Radiological Sciences have developed a machine learning algorithm that processes multispectral photon counting CT data for accurate medical imaging. Background: Photon counting computed tomography (PCCT) is a tremendous engineering advancement, enabling high resolution spectral imaging with myriad applications....

Published: 9/20/2024 | Inventor(s): Matthew Brown, Dieter Enzmann, John Hoffman, Michael Mcnitt-Gray

Keywords(s): AI algorithms, Algorithm, Algorithm Optical Coherence Tomography, algorithmic cancer detection, Artifical Intelligence (Machine Learning, Data Mining), artificial electromagnetic materials, Artificial Intelligence, artificial intelligence augmentation, Artificial Neural Network, Artificial Neural Network Artificial Neuron, artificial-intelligent materials, Big Data, Bladder Cancer, blood cancers, Brain cancer, Breast Cancer, Cancer, cancer antigen, cancer detection, Cancer Immunotherapy, Cancer stem cells, cancer target, Computed tomography, CT, Deep Learning, design software, Digital Pathology, generative artificial intelligence, Genetic Algorithm, Histopathological image analysis, Histopathology, histopathology images, hyperparameter optimization, Image Analysis, Image Processing, lympathic cancers, lymphatic cancer, Medical artificial intelligence (AI), Mesenchymal Stem Cell Derived Cancer Cells, Orthotopic cancer models, Pancreatic cancer, pathology image analysis, Photon counting computed tomography (PCCT), prostate cancer, Radiology, Radiology / Radiomitigation, Software, Software & Algorithms, Software Development Tools, Software-enabled learning

Category(s): Software & Algorithms, Software & Algorithms > Image Processing, Software & Algorithms > Artificial Intelligence & Machine Learning, Software & Algorithms > Data Analytics

2022-140 Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue Using Deep Learning

SUMMARY: UCLA researchers in the Departments of Bioengineering and Electrical and Computer Engineering have developed a deep learning-based algorithm that can detect and quantify the breast cancer marker human epidermal growth factor receptor 2 (HER2) in microscopic images without the need for time-consuming immunohistochemical staining (IHC). BACKGROUND:...

Published: 4/9/2024 | Inventor(s): Aydogan Ozcan, Yair Rivenson, Bijie Bai, Hongda Wang

Keywords(s): Artifical Intelligence (Machine Learning, Data Mining), Biomarker, Bladder Cancer, Breast Cancer, Cancer, Cancer Immunotherapy, Computer Aided Learning, Diagnostic Markers & Platforms, Diagnostic Test, Fluorescence, Fluorescent Labelling, HER2/Neu, Histology, Immune System, Immunohistochemistry, Life Science Research Tools, Machine Learning, Optics, Research Methods, Unsupervised Learning

Category(s): Life Science Research Tools, Life Science Research Tools > Research Methods, Diagnostic Markers, Diagnostic Markers > Immunology, Medical Devices > Medical Imaging, Medical Devices > Medical Imaging > Fluorescence, Software & Algorithms > Artificial Intelligence & Machine Learning, Optics & Photonics