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Extract from the Register of European Patents

EP About this file: EP4364102

EP4364102 - METHOD FOR TRAINING AND USING A DEEP LEARNING ALGORITHM TO COMPARE MEDICAL IMAGES BASED ON DIMENSIONALITY-REDUCED REPRESENTATIONS [Right-click to bookmark this link]
StatusRequest for examination was made
Status updated on  05.04.2024
Database last updated on 13.09.2024
FormerThe international publication has been made
Status updated on  06.01.2023
Formerunknown
Status updated on  16.07.2021
Most recent event   Tooltip30.08.2024Change: Validation statespublished on 02.10.2024 [2024/40]
30.08.2024Change - extension statespublished on 02.10.2024 [2024/40]
Applicant(s)For all designated states
Brainlab AG
Olof-Palme-Str. 9
81829 München / DE
[2024/19]
Inventor(s)01 / MOSER, Christoph
81829 Munich / DE
02 / BRIEU, Nicolas
81829 Munich / DE
03 / LIU, Qianyu
81829 Munich / DE
04 / PUCH GINER, Santiago
81829 Munich / DE
 [2024/19]
Representative(s)SSM Sandmair
Patentanwälte Rechtsanwalt
Partnerschaft mbB
Joseph-Wild-Straße 20
81829 München / DE
[2024/19]
Application number, filing date21737648.229.06.2021
[2024/19]
WO2021EP67878
Filing languageEN
Procedural languageEN
PublicationType: A1 Application with search report
No.:WO2023274512
Date:05.01.2023
Language:EN
[2023/01]
Type: A1 Application with search report 
No.:EP4364102
Date:08.05.2024
Language:EN
The application published by WIPO in one of the EPO official languages on 05.01.2023 takes the place of the publication of the European patent application.
[2024/19]
Search report(s)International search report - published on:EP05.01.2023
ClassificationIPC:G06V10/82, G06F18/40
[2024/19]
CPC:
G06V10/82 (EP); G06F18/213 (EP); G06F18/22 (EP);
G06V2201/03 (EP)
Designated contracting statesAL,   AT,   BE,   BG,   CH,   CY,   CZ,   DE,   DK,   EE,   ES,   FI,   FR,   GB,   GR,   HR,   HU,   IE,   IS,   IT,   LI,   LT,   LU,   LV,   MC,   MK,   MT,   NL,   NO,   PL,   PT,   RO,   RS,   SE,   SI,   SK,   SM,   TR [2024/19]
TitleGerman:VERFAHREN ZUM TRAINIEREN UND VERWENDUNG EINES TIEFENLERNALGORITHMUS ZUM VERGLEICH MEDIZINISCHER BILDER AUF DER BASIS VON DIMENSIONALITÄTSREDUZIERTEN DARSTELLUNGEN[2024/19]
English:METHOD FOR TRAINING AND USING A DEEP LEARNING ALGORITHM TO COMPARE MEDICAL IMAGES BASED ON DIMENSIONALITY-REDUCED REPRESENTATIONS[2024/19]
French:PROCÉDÉ D'APPRENTISSAGE ET D'UTILISATION D'UN ALGORITHME D'APPRENTISSAGE PROFOND POUR COMPARER DES IMAGES MÉDICALES SUR LA BASE DE REPRÉSENTATIONS À DIMENSIONNALITÉ RÉDUITE[2024/19]
Entry into regional phase09.01.2024National basic fee paid 
09.01.2024Designation fee(s) paid 
09.01.2024Examination fee paid 
Examination procedure09.01.2024Amendment by applicant (claims and/or description)
09.01.2024Examination requested  [2024/19]
09.01.2024Date on which the examining division has become responsible
Fees paidRenewal fee
09.01.2024Renewal fee patent year 03
25.06.2024Renewal fee patent year 04
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Responsibility for the accuracy, completeness or quality of the data displayed under the link provided lies entirely with the Unified Patent Court.
Cited inInternational search[XI]US2010329529  (FELDMAN MICHAEL D [US], et al) [X] 1-12,15,16,18,20,21 * paragraphs [0005] - [0011] - [0035] , [0063] , [0064] , [0 78] - [0085] - [0098] , [0109] - [0127] - [0131] , [0132] , [0151] , [0156] * * paragraphs [0192] , [0204] , [0206] , [0238] * [I] 13,14,17,19;
 [I]  - GHASSAN HAMARNEH ET AL, "Perception-Based Visualization of Manifold-Valued Medical Images Using Distance-Preserving Dimensionality Reduction", IEEE TRANSACTIONS ON MEDICAL IMAGING, IEEE, USA, (20110701), vol. 30, no. 7, doi:10.1109/TMI.2011.2111422, ISSN 0278-0062, pages 1314 - 1327, XP011356982 [I] 1-21 * Section III *

DOI:   http://dx.doi.org/10.1109/TMI.2011.2111422
 [I]  - "A Meta-Classifier for Detecting Prostate Cancer by Quantitative Integration of In Vivo Magnetic Resonance Spectroscopy and Magnetic Resonance Imaging", MEDICAL IMAGING 2008: COMPUTER-AIDED DIAGNOSIS, (20080317), XP040435015 [I] 1-21 * Section 2; Section 3; Section 4 *
 [I]  - TIWARI PALLAVI ET AL, Consensus-Locally Linear Embedding (C-LLE): Application to Prostate Cancer Detection on Magnetic Resonance Spectroscopy, ADVANCES IN BIOMETRICS : INTERNATIONAL CONFERENCE, ICB 2007, SEOUL, KOREA, AUGUST 27 - 29, 2007 ; PROCEEDINGS; [LECTURE NOTES IN COMPUTER SCIENCE; LECT.NOTES COMPUTER], SPRINGER, BERLIN, HEIDELBERG, PAGE(S) 330 - 338, (20080906), ISBN 978-3-540-74549-5, XP047462376 [I] 1-21 * Section 2; Section 3 *

DOI:   http://dx.doi.org/10.1007/978-3-540-85990-1_40
 [A]  - G. E. HINTON ET AL, "Reducing the Dimensionality of Data with Neural Networks", SCIENCE, US, (20060728), vol. 313, no. 5786, doi:10.1126/science.1127647, ISSN 0036-8075, pages 504 - 507, XP055313000 [A] 1-21 * the whole document *

DOI:   http://dx.doi.org/10.1126/science.1127647
by applicant   - I. GOODFELLOWY. BENGIOA. COURVILLE, Deep learning, chapter convolutional networks, (20160000), URL: http://www.deeplearningbook.org
    - J. WU, Introduction to convolutional neural networks, URL: https://pdfs.semanticscholar.org/450c/a19932fcef1ca6d0442cbf52fec38fb9d1e5.pdf
    - Common loss functions in machine learning, (20190822), URL: https://towardsdatascience.com/common-loss-functions-in-machine-learning-46af0ffc4d23
    - ALEX KRIZHEVSKYILYA SUTSKEVERGEOFFREY E. HINTON, Imagenet classification with deep convolutional neural networks, URL: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
    - S. RENK. HER. GIRSHICKJ. SUN, Faster r-cnn: Towards real-time object detection with region proposal networks, URL: https://arxiv.org/pdf/1506.01497.pdf
    - S.-E. WEIV. RAMAKRISHNAT. KANADEY. SHEIKH, Convolutional pose machines, URL: https://arxiv.0rg/pdf/1602.00134.pdf
    - JONATHAN LONGEVAN SHELHAMERTREVOR DARRELL, Fully convolutional networks for semantic segmentation, URL: https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf
    - HOFFER, E. et al., "Deep Metric Learning using Triplet Network", arXiv: 1412.6622
The EPO accepts no responsibility for the accuracy of data originating from other authorities; in particular, it does not guarantee that it is complete, up to date or fit for specific purposes.