Histopathology classification through machine self-learning of “tissue fingerprints”
US12354012B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 9, 2020 |
| Grant date | Jul 8, 2025 |
| Priority date | — |
| Expiry date | Oct 12, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16H20/10
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
Histologic classification of pathology specimens through machine learning is a nascent field which offers tremendous potential to improve cancer medicine. Its utility has been limited, in part because of differences in tissue preparation and the relative paucity of well-annotated images. We introduce tissue recognition, an unsupervised learning problem analogous to human face recognition, in which the goal is to identify individual tumors using a learned set of histologic features. This feature set is the “tissue fingerprint.” Because only specimen identities are matched to fingerprints, constructing an algorithm for producing them is a self-learning task that does not need image metadata annotations. Here, we provide an algorithm for self-learning tissue fingerprints, that, in conjunction with color normalization, can match hematoxylin and eosin stained tissues to one of 104 patients with 93% accuracy. We applied this identification network's internal representation as a tissue fingerprint for use in predicting the molecular status of an individual tumor (breast cancer clinical estrogen receptor (ER) status). We describe a fingerprint-based classifier that predicts ER status from …
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.