Memorial Sloan Kettering Cancer Center

Dig Vijay Kumar Yarlagadda

Thomas Fuchs

Peter Schueffler

Matthew Hanna

Peter J Schüffler

Chad Vanderbilt

Described are embodiments of a scalable system for processing whole slide images (WSIs). The system may heavily utilize parallel processing on both central processing units (CPUs) and graphics processing units (GPUs). Images may be decoded on a GPU. Reads may be unbuffered with a low memory footprint. Compute Unified Device Architecture (CUD A) operations may be employed to eliminate many inefficiencies in traditional WSI processing libraries.

Scalable and efficient whole slide image reader for deep learning and viewer applications

The present disclosure is directed to systems and methods for identifying regions of interest (ROIs) in images. A computing system may identify an image including an annotation defining an ROI. The image may have a plurality of pixels in a first color space. The computing system may convert the plurality of pixels from the first color space to a second color space to differentiate the annotation from the ROI. The computing system may select a first subset of pixels corresponding to the annotation based at least on a color value of the first subset of pixels in the second color space. The computing system may identify a second subset of pixels included in the ROI from the image using the first subset of pixels. The computing system may store an association between the second subset of pixels and the ROI defined by the annotation in the image.

Detection of annotated regions of interest in images

Described herein are systems and methods of determining primary sites from biomedical images. A computing system may identify a first biomedical image of a first sample from one of a primary site or a secondary site associated with a condition in a first subject. The computing system may apply the first biomedical image to a site prediction model comprising a plurality of weights to determine the primary site for the condition. The computing system may store an association between the first biomedical image and the primary site determined using the site prediction model.

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New York, NY, United States of America

Dig Vijay Kumar Yarlagadda