Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 515

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

A gentle introduction to deep learning in medical image processing

Open access
Maier, Andreas, Syben, Christopher, Lasser, Tobias, Riess, Christian·2019·Zeitschrift für Medizinische Physik
DOI 10.1016/j.zemedi.2018.12.003
Open this paper in CoChat — free account

Abstract

This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modeling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep ()learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.zemedi.2018.12.003

Cites

25 of 25 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 25 references

Research works printed but not linked: 35

  1. Mnih, 2015, Human-level control through deep reinforcement learning, Nature, 518, 529, 10.1038/nature14236 · DOI · Paper page
  2. Shen, 2017, Deep learning in medical image analysis, Annu Rev Biomed Eng, 19, 221, 10.1146/annurev-bioeng-071516-044442 · DOI · Paper page
  3. Suzuki, 2017, Survey of deep learning applications to medical image analysis, Med Imaging Technol, 35, 212
  4. Lakhani, 2018, Hello world deep learning in medical imaging, J Digit Imaging, 31, 283, 10.1007/s10278-018-0079-6 · DOI
  5. Kim, 2018, Prospects of deep learning for medical imaging, Precis Future Med, 2, 37, 10.23838/pfm.2018.00030 · DOI
  6. Ker, 2018, Deep learning applications in medical image analysis, IEEE Access, 6, 9375, 10.1109/ACCESS.2017.2788044 · DOI · Paper page
  7. Sahiner, 2018, Deep learning in medical imaging, Med Phys, 46, e1
  8. Niemann, 2013, vol. 4
  9. Hornik, 1991, Approximation capabilities of multilayer feedforward networks, Neural Netw, 4, 251, 10.1016/0893-6080(91)90009-T · DOI · Paper page
  10. Bengio, 2013, Representation learning: a review and new perspectives, IEEE Trans Pattern Anal Mach Intell, 35, 1798, 10.1109/TPAMI.2013.50 · DOI · Paper page
  11. Ivanova, 1995, Initialization of neural networks by means of decision trees, Knowl Based Syst, 8, 333, 10.1016/0950-7051(96)81917-4 · doi:10.1016/0950-7051(96)81917-4
  12. Goodfellow, 2016, vol. 1
  13. Maier, 2009, QMOS – a robust visualization method for speaker dependencies with different microphones, J Pattern Recognit Res, 4, 32
  14. Vincent, 2010, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, J Mach Learn Res, 11, 3371
  15. Aubreville, 2018, Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images, Int J Comput Assist Radiol Surg
  16. Ghesu, 2017, Multi-scale deep reinforcement learning for real-time 3D-landmark detection in CT scans, IEEE Trans Pattern Anal Mach Intell, 41, 176, 10.1109/TPAMI.2017.2782687 · DOI · Paper page
  17. Breininger, 2018, Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair, Int J Comput Assist Radiol Surg, 13, 10.1007/s11548-018-1779-6 · DOI · Paper page
  18. Middleton, 2004, Segmentation of magnetic resonance images using a combination of neural networks and active contour models, Med Eng Phys, 26, 71, 10.1016/S1350-4533(03)00137-1 · DOI
  19. Wu, 2016, Scalable high-performance image registration framework by unsupervised deep feature representations learning, IEEE Trans Biomed Eng, 63, 1505, 10.1109/TBME.2015.2496253 · DOI · Paper page
  20. De Fauw, 2018, Clinically applicable deep learning for diagnosis and referral in retinal disease, Nat Med, 24, 1342, 10.1038/s41591-018-0107-6 · DOI · Paper page
  21. Aubreville, 2017, Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning, Sci Rep, 7, 41598-017, 10.1038/s41598-017-12320-8 · DOI · Paper page
  22. Chu, 2017, Data-driven synthesis of smoke flows with CNN-based feature descriptors, ACM Trans Graph, 36, 69, 10.1145/3072959.3073643 · DOI
  23. Maier, 2018, Deep scatter estimation (DSE): feasibility of using a deep convolutional neural network for real-time X-ray scatter prediction in cone-beam CT, vol. 10573
  24. Han, 2017, MR-based synthetic CT generation using a deep convolutional neural network method, Med Phys, 44, 1408, 10.1002/mp.12155 · DOI · Paper page
  25. Wang, 2018, Image reconstruction is a new frontier of machine learning, IEEE Trans Med Imaging, 37, 1289, 10.1109/TMI.2018.2833635 · DOI · Paper page
  26. Zhang, 2018, A sparse-view CT reconstruction method based on combination of DenseNet and deconvolution, IEEE Trans Med Imaging, 37, 1407, 10.1109/TMI.2018.2823338 · DOI · Paper page
  27. Ye, 2018, Deep convolutional framelets: a general deep learning framework for inverse problems, SIAM J Imaging Sci, 11, 991, 10.1137/17M1141771 · DOI · Paper page
  28. Kang, 2018, Deep convolutional framelet denosing for low-dose CT via wavelet residual network, IEEE Trans Med Imaging, 37, 1358, 10.1109/TMI.2018.2823756 · DOI · Paper page
  29. Han, 2018, Framing U-Net via deep convolutional framelets: application to sparse-view CT, IEEE Trans Med Imaging, 37, 1418, 10.1109/TMI.2018.2823768 · DOI · Paper page
  30. Hammernik, 2018, Learning a variational network for reconstruction of accelerated mri data, Magn Reson Med, 79, 3055, 10.1002/mrm.26977 · DOI · Paper page
  31. Adler, 2018, Learned primal-dual reconstruction, IEEE Trans Med Imaging, 37, 1322, 10.1109/TMI.2018.2799231 · DOI · Paper page
  32. Würfl, 2018, Deep learning computed tomography: learning projection-domain weights from image domain in limited angle problems, IEEE Trans Med Imaging, 37, 1454, 10.1109/TMI.2018.2833499 · DOI · Paper page
  33. Zhang, 2018, Convolutional neural network based metal artifact reduction in X-ray computed tomography, IEEE Trans Med Imaging, 37, 1370, 10.1109/TMI.2018.2823083 · DOI · Paper page
  34. Li, 2018, Differentiable programming for image processing and deep learning in halide, ACM Trans Graph, 37, 139, 10.1145/3197517.3201383 · DOI
  35. Wang, 2016, A perspective on deep imaging, IEEE Access, 4, 8914, 10.1109/ACCESS.2016.2624938 · DOI · Paper page

Cited by

See all 515

Metadata

Authors
Maier, A., Syben, C., Lasser, T., & Riess, C.
Venue
Zeitschrift für Medizinische Physik
Publisher
Elsevier BV
Published
2019-05
Volume
29
Issue
2
Pages
86-101
ISSN
0939-3889 (ISSN-L)
DOI
10.1016/j.zemedi.2018.12.003
OpenAlex
W2897703124
Cited by
515
References
25
FWCI
33.63
Type
journal article
Language
en
Open access
hybrid
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A gentle introduction to deep learning in medical image processing. https://app.cochat.ai/papers/10.1016/j.zemedi.2018.12.003
The paper
Maier, A., Syben, C., Lasser, T., & Riess, C. (2019). A gentle introduction to deep learning in medical image processing. Zeitschrift für Medizinische Physik. https://doi.org/10.1016/j.zemedi.2018.12.003

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.