METHOD AND SYSTEM FOR DOMAIN KNOWLEDGE AUGMENTED MULTI-HEAD ATTENTION BASED ROBUST UNIVERSAL LESION DETECTION

State of the art deep network based Universal Lesion Detection (ULD) techniques inherently depend on large number of datasets for training the systems. Moreover, these system are specifically trained for lesion detection in organs of a Region of interest (RoI) of a body. Thus, requires retraining wh...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	SHARMA, MONIKA, DANI, MEGHAL, SHEORAN, MANU, VIG, LOVEKESH
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTING COUNTING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page
container_issue
container_start_page
container_title
container_volume
creator	SHARMA, MONIKA DANI, MEGHAL SHEORAN, MANU VIG, LOVEKESH
description	State of the art deep network based Universal Lesion Detection (ULD) techniques inherently depend on large number of datasets for training the systems. Moreover, these system are specifically trained for lesion detection in organs of a Region of interest (RoI) of a body. Thus, requires retraining when the RoI varies. Embodiments herein disclose a method and system for domain knowledge augmented multi-head attention based robust universal lesion detection. The method utilizes minimal number of Computer Tomography (CT) scan slices to extract maximum information using organ agnostic HU windows and a convolution augmented attention module for a computationally efficient ULD with enhanced prediction performance.
format	Patent
fullrecord	<record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2023177678A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2023177678A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2023177678A13</originalsourceid><addsrcrecordid>eNqNi80KgkAURt20iOodLrQWUiHdjs5Vh-YHvHeKViIxrfoR7P1JoQdo9cE551tHT4PcOgnCSqArMRqoXQfSGaEsnKy7aJQNgvCNQcsowXjNKm5RzCfmmSlnoRQ0q86Vnhi8VWfsSGjQSIuVyFgt3TZa3YfHFHa_3UT7Grlq4zC--zCNwy28wqf3lB7SLMnzY16IJPuv-gIcSDgw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>METHOD AND SYSTEM FOR DOMAIN KNOWLEDGE AUGMENTED MULTI-HEAD ATTENTION BASED ROBUST UNIVERSAL LESION DETECTION</title><source>esp@cenet</source><creator>SHARMA, MONIKA ; DANI, MEGHAL ; SHEORAN, MANU ; VIG, LOVEKESH</creator><creatorcontrib>SHARMA, MONIKA ; DANI, MEGHAL ; SHEORAN, MANU ; VIG, LOVEKESH</creatorcontrib><description>State of the art deep network based Universal Lesion Detection (ULD) techniques inherently depend on large number of datasets for training the systems. Moreover, these system are specifically trained for lesion detection in organs of a Region of interest (RoI) of a body. Thus, requires retraining when the RoI varies. Embodiments herein disclose a method and system for domain knowledge augmented multi-head attention based robust universal lesion detection. The method utilizes minimal number of Computer Tomography (CT) scan slices to extract maximum information using organ agnostic HU windows and a convolution augmented attention module for a computationally efficient ULD with enhanced prediction performance.</description><language>eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; PHYSICS</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230608&DB=EPODOC&CC=US&NR=2023177678A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230608&DB=EPODOC&CC=US&NR=2023177678A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>SHARMA, MONIKA</creatorcontrib><creatorcontrib>DANI, MEGHAL</creatorcontrib><creatorcontrib>SHEORAN, MANU</creatorcontrib><creatorcontrib>VIG, LOVEKESH</creatorcontrib><title>METHOD AND SYSTEM FOR DOMAIN KNOWLEDGE AUGMENTED MULTI-HEAD ATTENTION BASED ROBUST UNIVERSAL LESION DETECTION</title><description>State of the art deep network based Universal Lesion Detection (ULD) techniques inherently depend on large number of datasets for training the systems. Moreover, these system are specifically trained for lesion detection in organs of a Region of interest (RoI) of a body. Thus, requires retraining when the RoI varies. Embodiments herein disclose a method and system for domain knowledge augmented multi-head attention based robust universal lesion detection. The method utilizes minimal number of Computer Tomography (CT) scan slices to extract maximum information using organ agnostic HU windows and a convolution augmented attention module for a computationally efficient ULD with enhanced prediction performance.</description><subject>CALCULATING</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>eNqNi80KgkAURt20iOodLrQWUiHdjs5Vh-YHvHeKViIxrfoR7P1JoQdo9cE551tHT4PcOgnCSqArMRqoXQfSGaEsnKy7aJQNgvCNQcsowXjNKm5RzCfmmSlnoRQ0q86Vnhi8VWfsSGjQSIuVyFgt3TZa3YfHFHa_3UT7Grlq4zC--zCNwy28wqf3lB7SLMnzY16IJPuv-gIcSDgw</recordid><startdate>20230608</startdate><enddate>20230608</enddate><creator>SHARMA, MONIKA</creator><creator>DANI, MEGHAL</creator><creator>SHEORAN, MANU</creator><creator>VIG, LOVEKESH</creator><scope>EVB</scope></search><sort><creationdate>20230608</creationdate><title>METHOD AND SYSTEM FOR DOMAIN KNOWLEDGE AUGMENTED MULTI-HEAD ATTENTION BASED ROBUST UNIVERSAL LESION DETECTION</title><author>SHARMA, MONIKA ; DANI, MEGHAL ; SHEORAN, MANU ; VIG, LOVEKESH</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2023177678A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>SHARMA, MONIKA</creatorcontrib><creatorcontrib>DANI, MEGHAL</creatorcontrib><creatorcontrib>SHEORAN, MANU</creatorcontrib><creatorcontrib>VIG, LOVEKESH</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>SHARMA, MONIKA</au><au>DANI, MEGHAL</au><au>SHEORAN, MANU</au><au>VIG, LOVEKESH</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>METHOD AND SYSTEM FOR DOMAIN KNOWLEDGE AUGMENTED MULTI-HEAD ATTENTION BASED ROBUST UNIVERSAL LESION DETECTION</title><date>2023-06-08</date><risdate>2023</risdate><abstract>State of the art deep network based Universal Lesion Detection (ULD) techniques inherently depend on large number of datasets for training the systems. Moreover, these system are specifically trained for lesion detection in organs of a Region of interest (RoI) of a body. Thus, requires retraining when the RoI varies. Embodiments herein disclose a method and system for domain knowledge augmented multi-head attention based robust universal lesion detection. The method utilizes minimal number of Computer Tomography (CT) scan slices to extract maximum information using organ agnostic HU windows and a convolution augmented attention module for a computationally efficient ULD with enhanced prediction performance.</abstract><oa>free_for_read</oa></addata></record>
fulltext	fulltext_linktorsrc
identifier
ispartof
issn
language	eng
recordid	cdi_epo_espacenet_US2023177678A1
source	esp@cenet
subjects	CALCULATING COMPUTING COUNTING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS
title	METHOD AND SYSTEM FOR DOMAIN KNOWLEDGE AUGMENTED MULTI-HEAD ATTENTION BASED ROBUST UNIVERSAL LESION DETECTION
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-09T07%3A55%3A46IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=SHARMA,%20MONIKA&rft.date=2023-06-08&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2023177678A1%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true