US 12,451,236 B2
Method of analyzing X-ray images and report generation
Ashok Ajad, Uttar Pradesh (IN); Taniya Saini, Gujarat (IN); Ansuj Joshi, Madhya Pradesh (IN); and Swaroop Kumar Mysore Lokesh, Karnataka (IN)
Assigned to L&T TECHNOLOGY SERVICES LIMITED, Chennai (IN)
Appl. No. 18/011,114
Filed by L&T TECHNOLOGY SERVICES LIMITED, Chennai (IN)
PCT Filed Jun. 10, 2022, PCT No. PCT/IB2022/055395
§ 371(c)(1), (2) Date Dec. 16, 2022,
PCT Pub. No. WO2023/067397, PCT Pub. Date Apr. 27, 2023.
Claims priority of application No. 202141047320 (IN), filed on Oct. 19, 2021.
Prior Publication US 2024/0120070 A1, Apr. 11, 2024
Int. Cl. G06K 9/00 (2022.01); G06T 7/70 (2017.01); G06V 10/25 (2022.01); G06V 10/70 (2022.01); G16H 15/00 (2018.01); G16H 30/40 (2018.01)
CPC G16H 30/40 (2018.01) [G06T 7/70 (2017.01); G06V 10/25 (2022.01); G06V 10/70 (2022.01); G16H 15/00 (2018.01)] 15 Claims
OG exemplary drawing
 
1. A method of analyzing images and generating a report, the method comprising:
inputting, by an image analyzing device, a test image to a trained prediction model, wherein the prediction model is a deep learning-based model;
obtaining, by the image analyzing device, at least one abnormality associated with the test image, using the trained prediction model;
identifying, by the image analyzing device, at least one relevant predetermined region from a plurality of predetermined regions associated with the test image, using the trained prediction model, based on the at least one abnormality, wherein each of the at least one relevant predetermined region is identified based on hierarchical classification; and
determining, by the image analyzing device, within each of the at least one relevant predetermined region, a location associated with the at least one abnormality; and
obtaining the report, based on the at least one identified abnormality and the location associated with the at least one abnormality for each of the at least one relevant predetermined region, from a trained report generating model, wherein the report generating model is a deep learning-based model, wherein obtaining the report comprises:
inputting each of the at least one abnormality, latent embeddings associated with the test image, and the location associated with each of the at least one abnormality to the trained report generating model, wherein the report generating model is trained based on a Radiological Finding Quality Index (RFQI) loss with LSM loss; and
receiving the report for the abnormality from the report generation model.