Prospects of deep learning for medical imaging
Machine learning techniques are essential components of medical imaging research. Recently, a highly flexible machine learning approach known as deep learning has emerged as a disruptive technology to enhance the performance of existing machine learning techniques and to solve previously intractable...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sungkyunkwan University School of Medi
2018-06-01
|
| Series: | Precision and Future Medicine |
| Subjects: | |
| Online Access: | http://www.pfmjournal.org/upload/pdf/pfm-2018-00030.pdf |
| _version_ | 1811334845459070976 |
|---|---|
| author | Jonghoon Kim Jisu Hong Hyunjin Park |
| author_facet | Jonghoon Kim Jisu Hong Hyunjin Park |
| author_sort | Jonghoon Kim |
| collection | DOAJ |
| description | Machine learning techniques are essential components of medical imaging research. Recently, a highly flexible machine learning approach known as deep learning has emerged as a disruptive technology to enhance the performance of existing machine learning techniques and to solve previously intractable problems. Medical imaging has been identified as one of the key research fields where deep learning can contribute significantly. This review article aims to survey deep learning literature in medical imaging and describe its potential for future medical imaging research. First, an overview of how traditional machine learning evolved to deep learning is provided. Second, a survey of the application of deep learning in medical imaging research is given. Third, wellknown software tools for deep learning are reviewed. Finally, conclusions with limitations and future directions of deep learning in medical imaging are provided. |
| first_indexed | 2024-04-13T17:15:13Z |
| format | Article |
| id | doaj.art-fca9f11819514885b03c89975f493747 |
| institution | Directory Open Access Journal |
| issn | 2508-7940 2508-7959 |
| language | English |
| last_indexed | 2024-04-13T17:15:13Z |
| publishDate | 2018-06-01 |
| publisher | Sungkyunkwan University School of Medi |
| record_format | Article |
| series | Precision and Future Medicine |
| spelling | doaj.art-fca9f11819514885b03c89975f4937472022-12-22T02:38:10ZengSungkyunkwan University School of MediPrecision and Future Medicine2508-79402508-79592018-06-0122375210.23838/pfm.2018.0003032Prospects of deep learning for medical imagingJonghoon Kim0Jisu Hong1Hyunjin Park2 Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, KoreaMachine learning techniques are essential components of medical imaging research. Recently, a highly flexible machine learning approach known as deep learning has emerged as a disruptive technology to enhance the performance of existing machine learning techniques and to solve previously intractable problems. Medical imaging has been identified as one of the key research fields where deep learning can contribute significantly. This review article aims to survey deep learning literature in medical imaging and describe its potential for future medical imaging research. First, an overview of how traditional machine learning evolved to deep learning is provided. Second, a survey of the application of deep learning in medical imaging research is given. Third, wellknown software tools for deep learning are reviewed. Finally, conclusions with limitations and future directions of deep learning in medical imaging are provided.http://www.pfmjournal.org/upload/pdf/pfm-2018-00030.pdfDeep learningDiagnostic imagingMachine learning |
| spellingShingle | Jonghoon Kim Jisu Hong Hyunjin Park Prospects of deep learning for medical imaging Precision and Future Medicine Deep learning Diagnostic imaging Machine learning |
| title | Prospects of deep learning for medical imaging |
| title_full | Prospects of deep learning for medical imaging |
| title_fullStr | Prospects of deep learning for medical imaging |
| title_full_unstemmed | Prospects of deep learning for medical imaging |
| title_short | Prospects of deep learning for medical imaging |
| title_sort | prospects of deep learning for medical imaging |
| topic | Deep learning Diagnostic imaging Machine learning |
| url | http://www.pfmjournal.org/upload/pdf/pfm-2018-00030.pdf |
| work_keys_str_mv | AT jonghoonkim prospectsofdeeplearningformedicalimaging AT jisuhong prospectsofdeeplearningformedicalimaging AT hyunjinpark prospectsofdeeplearningformedicalimaging |