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Deep Learning Method For Tumor Cell Scoring On Cancer Biopsies
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Predicting Prostate Cancer Recurrence Using a Prognostic Model that Combines Immunohistochemical Staining and Gene Expression Profiling
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Using a First Stain to Train a Model to Predict the Region Stained by a Second Stain
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Tool that analyzes image data and generates and displays a confidence indicator along with a cancer score
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Identifying and excluding blurred areas of images of stained tissue to improve cancer scoring
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Methods for treatment and selection of patients responsive to immune mediated cancer therapy
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Detecting and visualizing correlations between measured correlation values and correlation reference values of a pathway
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System for predicting the recurrence of cancer in a cancer patient
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Applying pixelwise descriptors to a target image that are generated by segmenting objects in other images
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Immunotherapy Using a Logical AND Combination for Immune Response Activation
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Grading of glandular tissue cancer by detailed image analysis of stained tissue slices
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Updating landmarks to improve coregistration as regions of interest are corrected
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Learning pixel visual context from object characteristics to generate rich semantic images
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Generating image-based diagnostic tests by optimizing image analysis and data mining of co-registered images
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Automatic image analysis and quantification for fluorescence in situ hybridization
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Coregistering images of needle biopsies using multiple weighted landmarks
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Gleason grading by segmenting and combining co-registered images of differently stained tissue slices
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Evaluation of co-registered images of differently stained tissue slices
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Biomarker evaluation through image analysis
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Generating artificial hyperspectral images using correlated analysis of co-registered images
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