Memorial Sloan Kettering Cancer Center had 13 patents in artificial intelligence during Q4 2023. The Memorial Sloan Kettering Cancer Center has filed patents for systems and methods to detect blur in digital images, predictive evaluation of petroleum product containers, retrieval and analysis of structured and unstructured medical data for treatment decisions, reinforcement learning for localization and classification on biomedical images, and training models to segment images with user feedback. GlobalData’s report on Memorial Sloan Kettering Cancer Center gives a 360-degreee view of the company including its patenting strategy. Buy the report here.

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Memorial Sloan Kettering Cancer Center grant share with artificial intelligence as a theme is 7% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: System, method and computer-accessible medium for quantification of blur in digital images (Patent ID: US20230410460A1)

The patent filed by the Memorial Sloan Kettering Cancer Center discusses systems and methods for detecting blur in digital images, particularly in the context of pathology and slide scanners. The solution involves generating patches from digital images, determining sharpness metrics for each patch, and using a patch classifier, such as a neural network, to assign a blur score to each patch. The system can also utilize algorithms like random forest regression or logistic regression to determine blur scores, as well as a residual neural network. Additionally, the system includes a background detector to discard patches with background data and can convert images to grayscale. The sharpness metrics used include pixel intensity-based, gradient-based, transform-based, and perceptual-based features, with specific metrics like variance, entropy histogram, gradient, and various blur metrics in the frequency domain.

Furthermore, the system can flag patches with blur scores above a predetermined threshold and generate a blur map based on these flagged patches. The method involves similar steps of generating patches, determining sharpness metrics, assigning blur scores using a neural network, and generating a blur map. It also includes discarding patches with background data, converting images to grayscale, and flagging patches above a certain blur score threshold. The system and method aim to improve the quality control of scanned images by automatically rescanning blurry images and identifying regions of images affected by blur, ultimately enhancing the accuracy and reliability of digital image analysis in medical settings.

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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.