A számítógépes mélytanulási technológia várható megjelenése a hazai mammográfiában

Ribli Dezső, Zsuppán Richárd, Pollner Péter, Horváth Anna, Bánsághi Zoltán, I. Csabai, V. Bérczi, Unger Zsuzsa

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Abstract

Introduction and aim: The technology, named 'deep learning' is the promising result of the last two decades of development in computer science. It poses an unavoidable challenge for medicine, how to understand, apply and adopt the - today not fully explored - possibilities that have become available by these new methods. Method: It is a gift and a mission, since the exponentially growing volume of raw data (from imaging, laboratory, therapy diagnostics or therapy interactions, etc.) did not solve until now our wished and aimed goal to treat patients according to their personal status and setting or specific to their tumor and disease. Results: Currently, as a responsible health care provider and financier, we face the problem of supporting suboptimal procedures and protocols either at individual or at community level. The problem roots in the overwhelming amount of data and, at the same time, the lack of targeted information for treatment. We expect from the deep learning technology an aid which helps to reinforce and extend the human-human cooperations in patient-doctor visits. We expect that computers take over the tedious work allowing to revive the core of healing medicine: the insightful meeting and discussion between patients and medical experts. Conclusion: We should learn the revelational possibilities of deep learning techniques that can help to overcome our recognized finite capacities in data processing and integration. If we, doctors and health care providers or decision makers, are able to abandon our fears and prejudices, then we can utilize this new tool not only in imaging diagnostics but also for daily therapies (e.g., immune therapy). The paper aims to make a great mind to do this.

???translatedTitle???Potential applications of deep learning-based technologies in Hungarian mammography
???language???Hungarian
???pages???138-143
???numberOfPages???6
???journalAssociation???Orvosi hetilap
???volume???160
???journalNumber???4
???DOIs???
???publicationStatus???Published - jan. 1 2019

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User-Computer Interface
Artificial Intelligence
Mammography
Learning
Technology
Health Personnel
Medicine
Therapeutics
Gift Giving
Diagnostic Imaging
Patient Compliance
Fear
Machine Learning
Neoplasms

Keywords

  • Artificial intelligence
  • Machine learning
  • Mammography
  • User-computer interface

ASJC Scopus subject areas

  • Medicine(all)

Cite this

A számítógépes mélytanulási technológia várható megjelenése a hazai mammográfiában. / Dezső, Ribli; Richárd, Zsuppán; Péter, Pollner; Anna, Horváth; Zoltán, Bánsághi; Csabai, I.; Bérczi, V.; Zsuzsa, Unger.

???in???: Orvosi hetilap, ???journal.volume??? 160, ???journalNumber??? 4, 01.01.2019, ???pages??? 138-143.

???family-name???: Article

Dezső, R, Richárd, Z, Péter, P, Anna, H, Zoltán, B, Csabai, I, Bérczi, V & Zsuzsa, U 2019, 'A számítógépes mélytanulási technológia várható megjelenése a hazai mammográfiában', Orvosi hetilap, ???journal.volume??? 160, ???journalNumber??? 4, ???pages??? 138-143. https://doi.org/10.1556/650.2019.31263
Dezső, Ribli ; Richárd, Zsuppán ; Péter, Pollner ; Anna, Horváth ; Zoltán, Bánsághi ; Csabai, I. ; Bérczi, V. ; Zsuzsa, Unger. / A számítógépes mélytanulási technológia várható megjelenése a hazai mammográfiában. ???in???: Orvosi hetilap. 2019 ; ???journal.volume??? 160, ???journalNumber??? 4. ???pages??? 138-143.
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abstract = "Introduction and aim: The technology, named 'deep learning' is the promising result of the last two decades of development in computer science. It poses an unavoidable challenge for medicine, how to understand, apply and adopt the - today not fully explored - possibilities that have become available by these new methods. Method: It is a gift and a mission, since the exponentially growing volume of raw data (from imaging, laboratory, therapy diagnostics or therapy interactions, etc.) did not solve until now our wished and aimed goal to treat patients according to their personal status and setting or specific to their tumor and disease. Results: Currently, as a responsible health care provider and financier, we face the problem of supporting suboptimal procedures and protocols either at individual or at community level. The problem roots in the overwhelming amount of data and, at the same time, the lack of targeted information for treatment. We expect from the deep learning technology an aid which helps to reinforce and extend the human-human cooperations in patient-doctor visits. We expect that computers take over the tedious work allowing to revive the core of healing medicine: the insightful meeting and discussion between patients and medical experts. Conclusion: We should learn the revelational possibilities of deep learning techniques that can help to overcome our recognized finite capacities in data processing and integration. If we, doctors and health care providers or decision makers, are able to abandon our fears and prejudices, then we can utilize this new tool not only in imaging diagnostics but also for daily therapies (e.g., immune therapy). The paper aims to make a great mind to do this.",
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AU - Richárd, Zsuppán

AU - Péter, Pollner

AU - Anna, Horváth

AU - Zoltán, Bánsághi

AU - Csabai, I.

AU - Bérczi, V.

AU - Zsuzsa, Unger

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AB - Introduction and aim: The technology, named 'deep learning' is the promising result of the last two decades of development in computer science. It poses an unavoidable challenge for medicine, how to understand, apply and adopt the - today not fully explored - possibilities that have become available by these new methods. Method: It is a gift and a mission, since the exponentially growing volume of raw data (from imaging, laboratory, therapy diagnostics or therapy interactions, etc.) did not solve until now our wished and aimed goal to treat patients according to their personal status and setting or specific to their tumor and disease. Results: Currently, as a responsible health care provider and financier, we face the problem of supporting suboptimal procedures and protocols either at individual or at community level. The problem roots in the overwhelming amount of data and, at the same time, the lack of targeted information for treatment. We expect from the deep learning technology an aid which helps to reinforce and extend the human-human cooperations in patient-doctor visits. We expect that computers take over the tedious work allowing to revive the core of healing medicine: the insightful meeting and discussion between patients and medical experts. Conclusion: We should learn the revelational possibilities of deep learning techniques that can help to overcome our recognized finite capacities in data processing and integration. If we, doctors and health care providers or decision makers, are able to abandon our fears and prejudices, then we can utilize this new tool not only in imaging diagnostics but also for daily therapies (e.g., immune therapy). The paper aims to make a great mind to do this.

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