Medical object detection and identification via machine learning
An approach for improving object detection performance by using bilateral organ information disclosed. The approach comprises introducing a penalty term to an object localization model. The penalty term encourages the model to identify same physical regions (actual same physical location in the pati...
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creator | Ratner, Vadim Shoshan, Yoel |
description | An approach for improving object detection performance by using bilateral organ information disclosed. The approach comprises introducing a penalty term to an object localization model. The penalty term encourages the model to identify same physical regions (actual same physical location in the patient's body) in multiple images, which create an image of the same organ. The approach includes a sub-component of the model that can output an entity embedding, in additional to the conventional classification and localization prediction. During optimization process, similar entity embedding for the same logical entity are encouraged, and similar entity embedding for different logical entities are discouraged. |
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The approach comprises introducing a penalty term to an object localization model. The penalty term encourages the model to identify same physical regions (actual same physical location in the patient's body) in multiple images, which create an image of the same organ. The approach includes a sub-component of the model that can output an entity embedding, in additional to the conventional classification and localization prediction. 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The approach comprises introducing a penalty term to an object localization model. The penalty term encourages the model to identify same physical regions (actual same physical location in the patient's body) in multiple images, which create an image of the same organ. The approach includes a sub-component of the model that can output an entity embedding, in additional to the conventional classification and localization prediction. During optimization process, similar entity embedding for the same logical entity are encouraged, and similar entity embedding for different logical entities are discouraged.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZHDwTU3JTE7MUchPykpNLlFISS0BUpn5eQqJeSkKmSmpeSWZaUAFYKGyzESF3MTkjMy8VIWc1MSivMy8dB4G1rTEnOJUXijNzaDo5hri7KGbWpAfn1pckJicmpdaEh8abGhoZmxmamnkZGRMjBoAzdExhA</recordid><startdate>20230425</startdate><enddate>20230425</enddate><creator>Ratner, Vadim</creator><creator>Shoshan, Yoel</creator><scope>EVB</scope></search><sort><creationdate>20230425</creationdate><title>Medical object detection and identification via machine learning</title><author>Ratner, Vadim ; Shoshan, Yoel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US11636592B23</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>Ratner, Vadim</creatorcontrib><creatorcontrib>Shoshan, Yoel</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ratner, Vadim</au><au>Shoshan, Yoel</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Medical object detection and identification via machine learning</title><date>2023-04-25</date><risdate>2023</risdate><abstract>An approach for improving object detection performance by using bilateral organ information disclosed. The approach comprises introducing a penalty term to an object localization model. The penalty term encourages the model to identify same physical regions (actual same physical location in the patient's body) in multiple images, which create an image of the same organ. The approach includes a sub-component of the model that can output an entity embedding, in additional to the conventional classification and localization prediction. During optimization process, similar entity embedding for the same logical entity are encouraged, and similar entity embedding for different logical entities are discouraged.</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS |
title | Medical object detection and identification via machine learning |
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