Search Results - magnetic+resonance+imaging+pathology

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The Growing Role of Digital Pathology and Machine Learning in Cancer Diagnostics (UCLA Case No. 2021-134)

Summary: Researchers at UCLA's Department of Radiological Sciences have developed an active learning methodology for digital pathology image analysis, addressing challenges posed by inconsistent annotations and revolutionizing the training of diagnostic algorithms. Background: The global digital pathology market is anticipated to experience significant...

Published: 8/9/2023 | Inventor(s): Corey Arnold, Wenyuan Li, Jiayun Li, William Speier

Keywords(s): active learning, Artifical Intelligence (Machine Learning, Data Mining), Artificial Intelligence, Artificial Neural Network, artificial-intelligent materials, computational imaging task, convolutional networks, curriculum learning, Deep Learning, deep physical neural network, Digital Pathology, dynamic identification, Histopathological image analysis, histopathology images, Image Processing, Magnetic Resonance Imaging Pathology, Medical Devices and Materials, Medical Imaging, noisy annotations, noisy label detection, pathology image analysis

Category(s): Software & Algorithms, Software & Algorithms > Image Processing, Electrical > Imaging, Medical Devices > Medical Imaging

Multi-Resolution Model With Attention for Whole Slide Image Analysis Trained Using Weak Labels (UCLA Case No. 2019-943)

Summary: UCLA Researchers in the Department of Radiology have developed a computer-aided diagnostic model that uses histology slides to interpret and detect prostate cancer diagnoses with high levels of accuracy. Background: In the United States, prostate cancer ranks as the most prevalent and second deadliest cancer among men. Diagnosing this...

Published: 7/10/2024 | Inventor(s): Corey Arnold, Jiayun Li, William Speier

Keywords(s): Artifical Intelligence (Machine Learning, Data Mining), Artificial Intelligence, Artificial Neural Network, Digital Health, Digital Holography, Digital immunohistology, Digital Pathology, Machine Learning, Magnetic Resonance Imaging Pathology, MRI

Category(s): Software & Algorithms, Software & Algorithms > Image Processing, Software & Algorithms > Digital Health, Software & Algorithms > Artificial Intelligence & Machine Learning, Diagnostic Markers, Medical Devices, Medical Devices > Medical Imaging, Medical Devices > Medical Imaging > MRI, Optics & Photonics

2022-088 Neural Networks for Adipose Tissue Segmentation on Magnetic Resonance Images

Summary: UCLA researchers in the Department of Radiological Sciences have developed a novel neural network that can segment and quantify visceral and subcutaneous adipose tissues on magnetic resonance images automatically, accurately, and rapidly, to enhance clinical methods for characterizing and monitoring risk for cardiometabolic diseases in patients...

Published: 9/25/2024 | Inventor(s): Holden Wu, Sevgi Gokce Kafali-Tekat

Keywords(s): Advanced Computing / AI, Artificial Neural Network, Artificial Neural Network Artificial Neuron, cardiometabolic disease, Hypertension, Imaging, Machine Learning, Magnetic Resonance Imaging, Magnetic Resonance Imaging Pathology, Medical Imaging, MRI, multi-contrast imaging, obesity, Tissue (Biology), tissue annotation, tissue detection, tissue ribbons alignment, tissue segmentation

Category(s): Medical Devices > Medical Imaging, Platforms > Diagnostic Platform Technologies, Medical Devices > Medical Imaging > MRI, Therapeutics > Metabolism And Endocrinology

2014-9AG Wide-Field Computational Imaging of Pathology Slides Using Lensfree On-Chip Microscopy

Summary: UCLA researchers in the Department of Electrical and Computer Engineering have developed a wide field-of-view (FOV) computational imaging method using lens-free on-chip microscopy to efficiently and cost-effectively scan biological samples such as pathology slides. Background: Wide field-of-view (FOV) and high-resolution imaging is crucial...

Published: 7/19/2023 | Inventor(s): Aydogan Ozcan, Alon Greenbaum, Yibo Zhang, Alborz Feizi, Wei Luo

Category(s): Life Science Research Tools, Electrical, Electrical > Imaging, Life Science Research Tools > Microscopy And Imaging, Software & Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning, Diagnostic Markers, Diagnostic Markers > Targets And Assays, Medical Devices > Medical Imaging

2019-737 Deep Learning-Based Color Holographic Microscopy

Summary: UCLA researchers in the Department of Electrical Engineering have developed a novel deep learning-based method that performs high-fidelity color image reconstruction using a single hologram. Background: Pathology slides are currently the gold standard in diagnostics for many diseases. Accurate color representations of well stained pathology...

Published: 7/19/2023 | Inventor(s): Aydogan Ozcan, Yair Rivenson, Tairan Liu, Yibo Zhang, Zhensong Wei

Keywords(s): Artifical Intelligence (Machine Learning, Data Mining), Artificial Neural Network, Computer Aided Learning, Confocal Microscopy, Cytopathology, Digital Holography, Electron Microscope, Fluorescence Microscope, Fluorescence-Lifetime Imaging Microscopy Leading Lights, Histology, Histopathology, Holography, Image Resolution, Imaging, Immunohistochemistry, Machine Learning, Magnetic Resonance Imaging Pathology, Medical Imaging, Microscope, Microscopy, Microscopy And Imaging, Neuropathology, Pathogen, Pathogenesis, Pathophysiology, Perceptual Learning, Unsupervised Learning , Wavelength

Category(s): Electrical, Electrical > Imaging, Life Science Research Tools, Life Science Research Tools > Microscopy And Imaging, Software & Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning, Diagnostic Markers, Medical Devices > Medical Imaging