The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jul. 14, 2026
Filed:
Jul. 20, 2023
Pathal, Inc., Boston, MA (US);
Harsha Vardhan Pokkalla, Sudbury, MA (US);
Hunter L. Elliott, Boston, MA (US);
Dayong Wang, Wellesley, MA (US);
Benjamin P. Glass, Boston, MA (US);
Ilan N. Wapinski, Brookline, MA (US);
Jennifer K. Kerner, Brookline, MA (US);
Andrew H. Beck, Brookline, MA (US);
Aditya Khosla, Lexington, MA (US);
Sai Chowdary Gullapally, Boston, MA (US);
Ramprakash Srinivasan, Brookline, MA (US);
Ylaine Gerardin, Cambridge, MA (US);
John Shamshoian, Wilmington, MA (US);
John Abel, Boston, MA (US);
Ciyue Shen, Boston, MA (US);
PathAI, Inc., Boston, MA (US);
Abstract
In some aspects, the described systems and methods provide for validating performance of a model trained on a plurality of annotated pathology images. In validating the trained model, frames may be sampled from one or more pathology images. Each frame may include a distinct portion of a pathology image. Reference annotations on the frames may be received from a plurality of users, each reference annotation describing at least one of a plurality of tissue or cellular characteristic categories or other biological objects for a frame. The frames may be processed using the trained model to generate model predictions, each model prediction describing at least one of the tissue or cellular characteristic categories for a processed frame. Performance of the trained model may be validated based on associating the model predicted annotations with the reference annotations across the one or more pathology images from the plurality of users.