Ovarian toxicity assessment in histopathological images using deep learning
US12067716B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Oct 14, 2021 |
| Grant date | Aug 20, 2024 |
| Priority date | — |
| Expiry date | Jun 22, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/031
- WIPO fieldMedical technology
- WIPO sectorInstruments
Abstract
The present disclosure relates to a deep learning neural network that can identify corpora lutea in the ovaries and a rules-based technique that can count the corpora lutea identified in the ovaries and infer an ovarian toxicity of a compound based on the count of the corpora lutea (CL). Particularly, aspects of the present disclosure are directed to obtaining a set of images of tissue slices from ovaries treated with an amount of a compound; generating, using a neural network model, the set of images with a bounding box around objects that are identified as the CL within the set of images based on coordinates predicted for the bounding box; counting the bounding boxes within the set of images to obtain a CL count for the ovaries; and determining an ovarian toxicity of the compound at the amount based on the CL count.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.