Paige.AI has patented a method for processing electronic slide images associated with tissue specimens. The system partitions images into tiles, detects tissue regions, removes non-tissue tiles, and uses machine learning to predict labels. Training involves creating tissue masks and inferring predictions from synoptic annotations. GlobalData’s report on Paige.AI gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Paige.AI, AI assisted radiology was a key innovation area identified from patents. Paige.AI's grant share as of May 2024 was 10%. Grant share is based on the ratio of number of grants to total number of patents.
Computer-implemented method for processing electronic slide images
A recently granted patent (Publication Number: US11995903B2) discloses a computer-implemented method for processing electronic slide images associated with tissue specimens. The method involves generating a machine learning prediction model by partitioning training images into training tiles, creating a training tissue mask, and inferring tile-level predictions using synoptic annotations. The model utilizes weak supervision techniques like multiple-instance learning and unsupervised clustering to predict labels for patient specimens based on the training tiles. Additionally, the system includes operations for detecting tissue regions, segmenting images based on color and texture features, and predicting various types of labels, including binary, categorical, ordinal, or real-valued.
Furthermore, the patent describes a system and a non-transitory computer-readable medium storing instructions for implementing the method. The system comprises a memory storing instructions and a processor executing operations to process electronic slide images by generating a machine learning prediction model, detecting tissue regions, and predicting labels using weak supervision techniques. The method outlined in the patent aims to enhance the processing of electronic slide images associated with tissue specimens by leveraging machine learning models and advanced segmentation techniques. By automating the analysis of slide images and predicting labels for patient specimens, this technology has the potential to streamline medical diagnostics and improve the efficiency of pathology workflows.
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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.
