公开(公告)号：US20240153073A1

公开(公告)日：2024-05-09

申请号：US18280662

申请日：2022-03-07

Applicant: DEEP BIO INC.

Inventor： Joon Young CHO ,  Hye yoon CHANG ,  Tae Yeong Kwak ,  Sun woo KIM

IPC: G06T7/00 ,  G16H30/20 ,  G16H50/20

CPC classification number: G06T7/0012 ,  G16H30/20 ,  G16H50/20 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30081 ,  G06T2207/30096

Abstract: A method for training an artificial neural network for detecting a prostate cancer from a TURP pathological image, includes: acquires a plurality of acquiring pathological images for primary training, each being a prostate needle biopsy pathological image or a radical prostatectomy pathological image; using the pathological images to primarily train an artificial neural network for determining prostate cancer; acquiring TURP pathological images; and using the TURP pathological images to secondarily train the primarily trained artificial neural network, wherein each TURP pathological image includes a non-prostate tissue region and/or a cauterized prostate tissue region, and does not include any prostate cancer lesion region.

公开(公告)号：US11798686B2

公开(公告)日：2023-10-24

申请号：US17282775

申请日：2019-10-04

Applicant: DEEP BIO INC.

Inventor： Tae Yeong Kwak ,  Sang Hun Lee ,  Sun Woo Kim

IPC: G06K9/00 ,  G16H50/20 ,  G16H30/40 ,  G06N3/088 ,  G06T7/00 ,  G06N3/045 ,  G06F18/214

CPC classification number: G16H50/20 ,  G06N3/045 ,  G06N3/088 ,  G06T7/0014 ,  G16H30/40 ,  G06F18/2148 ,  G06T2207/30081 ,  G06V2201/03

Abstract: A system for searching for a pathological image includes: an autoencoder having an encoder for receiving an original pathological image and extracting a feature of the original pathological image, and a decoder for receiving the feature of the original pathological image extracted by the encoder and generating a reconstructed pathological image corresponding to the original pathological image; a diagnostic neural network for receiving the reconstructed pathological image generated by the autoencoder that has received the original pathological image, and outputting a diagnosis result of a predetermined disease; and a training module for training the autoencoder and the diagnostic neural network by inputting a plurality of training pathological images, each labeled with a diagnosis result, into the autoencoder. The autoencoder is trained by reflecting the diagnosis result of the reconstructed pathological image output from the diagnostic neural network.