The DOI matches its Crossref record; 301 of the 301 references it cites were found in a registry or an index.
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions
Open accessAbstract
Medical image analysis is currently experiencing a paradigm shift due to deep learning. This technology has recently attracted so much interest of the Medical Imaging Community that it led to a specialized conference in “Medical Imaging with Deep Learning” in the year 2018. This paper surveys the recent developments in this direction and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying pattern recognition tasks and further sub-categorize it following a taxonomy based on human anatomy. This paper does not assume prior knowledge of deep learning and makes a significant contribution in explaining the core deep learning concepts to the non-experts in the Medical Community. This paper provides a unique computer vision/machine learning perspective taken on the advances of deep learning in medical imaging. This enables us to single out “lack of appropriately annotated large-scale data sets” as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of computer vision, pattern recognition, and machine learning, where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging Community to fully harness deep learning 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.
Integrity signals
Registry
Cites
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.
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
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Research works printed but not linked: 25
- sobel, 1968, A $3\times3$ isotropic gradient operator for image processing, Proc Talk Stanford Artif Project, 271
- tanner, 2018, Generative adversarial networks for MR-CT deformable image registration, arXiv 1807 07349
- gutman, 2016, Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC), arXiv 1605 01397
- 10.1118/1.597891 · doi:10.1118/1.597891
- combalia, 2018, Monte-carlo sampling applied to multiple instance learning for whole slide image classification, Proc 1st Conf Med Imag Deep Learn (MIDL), 1
- sabour, 2017, Dynamic routing between capsules, Proc Adv Neural Inf Process Syst, 3856
- roth, 2018, Deep learning and its application to medical image segmentation, Med Imag Technol, 36, 63
- al-rfou, 2016, Theano: A Python framework for fast computation of mathematical expressions, arXiv 1605 02688
- chollet, 2015, Keras
- heinrich, 2018, OBELISK—One kernel to solve nearly everything: Unified 3D binary convolutions for image analysis, Proc 1st Conf Med Imag Deep Learn (MIDL)
- schalkoff, 1997, Artificial Neural Networks, 1
- yi, 2018, Generative adversarial network in medical imaging: A review, arXiv 1809 07294
- kazeminia, 2018, GANs for medical image analysis, arXiv 1809 06222
- goodfellow, 2014, Generative adversarial nets, Proc Adv Neural Inf Process Syst, 2672
- 2018, FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems
- 10.1007/978-3-319-68127-6_7 · DOI
- makhzani, 2015, Adversarial autoencoders, arXiv 1511 05644
- alaverdyan, 2018, Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening, Proc 1st Conf Med Imag Deep Learn (MIDL)
- zhang, 2018, Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images, arXiv 1807 02152
- radau, 2009, Evaluation framework for algorithms segmenting short axis cardiac MRI, The MIDAS Journal - Cardiac MR Left Ventricle Segmentation Challenge, 49, 1
- 10.1016/j.compeleceng.2018.11.024 · doi:10.1016/j.compeleceng.2018.11.024
- rajpurkar, 2017, MURA: Large dataset for abnormality detection in musculoskeletal radiographs, arXiv 1712 06957
- 10.1117/12.274179 · doi:10.1117/12.274179
- 10.1016/j.ijleo.2015.06.011 · doi:10.1016/j.ijleo.2015.06.011
- elmasry, 2012, Graph partitioning based automatic segmentation approach for CT scan liver images, Proc Federated Conf Comput Sci Inf Syst (FedCSIS), 183
Cited by
A Review of Deep-Learning-Based Medical Image Segmentation Methods
Liu, X., Song, L., Liu, S., & Zhang, Y. (2021) · Sustainability · doi:10.3390/su13031224 · Cited by 868
Towards a guideline for evaluation metrics in medical image segmentation
Müller, D., Soto-Rey, I., & Kramer, F. (2022) · BMC Research Notes · doi:10.1186/s13104-022-06096-y · Cited by 668
Transfer learning techniques for medical image analysis: A review
Kora, P., Ooi, C. P., Faust, O., Raghavendra, U., Gudigar, A., Chan, W. Y., Meenakshi, K., Swaraja, K., Plawiak, P., & Rajendra Acharya, U. (2022) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2021.11.004 · Cited by 367
Literature review: efficient deep neural networks techniques for medical image analysis
Abdou, M. A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-06960-9 · Cited by 312
Deep learning in multi-object detection and tracking: state of the art
Pal, S. K., Pramanik, A., Maiti, J., & Mitra, P. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02293-7 · Cited by 298
Metadata
- Authors
- Altaf, F., Islam, S. M. S., Akhtar, N., & Janjua, N. K.
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 99540-99572
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2911798429
- Cited by
- 286
- References
- 301
- FWCI
- 14.97
- Type
- journal article
- Language
- en
- Open access
- gold
- Funders
- Australian Government Research Training Program Scholarship
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: Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions. https://app.cochat.ai/papers/10.1109/access.2019.2929365
- The paper
- Altaf, F., Islam, S. M. S., Akhtar, N., & Janjua, N. K. (2019). Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions. IEEE Access. https://doi.org/10.1109/access.2019.2929365
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.
