An overview of deep learning in medical imaging focusing on MRI

Alexander Selvikvåg LundervoldArvid Lundervold

article2018Zeitschrift für Medizinische Physik1,996 citations

Surveys deep learning applications across the complete magnetic resonance imaging workflow, spanning image acquisition, segmentation, and disease prediction, while providing key open-source code and benchmark datasets for emerging practitioners.

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Healthcare providers generate massive volumes of complex medical data at a pace that far outstrips traditional analytical methods. Recent breakthroughs in machine learning, particularly deep artificial neural networks, have set new performance benchmarks across computer vision and data processing. The article provides an executive overview of recent developments in deep learning, focusing specifically on its end-to-end integration across the magnetic resonance imaging workflow and identifying key technical resources and hurdles.

The article conducts a comprehensive state-of-the-art review to examine how deep learning methods impact every stage of medical imaging. The authors evaluate deep learning applications across foundational signal processing—such as acquisition, image reconstruction, quantitative tissue parameter mapping, denoising, and registration—as well as downstream clinical tasks like organ segmentation, disease classification, and automated reporting across organs including the brain, kidney, prostate, and spine.

The key findings reveal that deep learning has transformed image processing by learning complex hierarchical features directly from raw data rather than relying on manual feature engineering. In image reconstruction and quantitative parameter mapping, deep neural networks accelerate processing dramatically; for example, deep learning models for magnetic resonance fingerprinting reconstruct tissue maps hundreds to thousands of times faster than traditional dictionary-matching methods. Deep models also achieve near-instantaneous deformable image registration and enable significant contrast dose reductions without sacrificing diagnostic quality. Downstream, specialized architectures like convolutional networks consistently deliver superior performance over legacy approaches in anatomical segmentation and disease diagnosis.

These advances indicate that deep learning will become a standard foundation for medical image analysis, offering substantial opportunities to reduce hospital operational costs, shorten scan times, lower radiation or contrast agent risks, and enhance diagnostic accuracy. However, realizing these clinical benefits requires moving beyond purely technical development to solve complex workflow integration, establish end-user trust, and comply with medical safety regulations.

To successfully adopt these technologies, healthcare leaders and research organizations should invest in open-science practices, leverage transfer learning and data augmentation to handle limited medical data, and integrate clinical end-users early in the system design loop. Organizations should also adopt privacy-preserving techniques such as federated learning to enable collaborative model training across institutions without exposing sensitive patient records.

Confidence in deep learning's raw technical capabilities is high, but significant caution is required before full clinical deployment. The technology remains constrained by high demands for large, representative labeled datasets, potential performance drops when exposed to messy real-world clinical data, and the interpretability challenge posed by black-box algorithms. Developing explainable models and incorporating robust uncertainty estimation will be essential to ensure patient safety and clinical reliability.

Haukeland University Hospital Mohn Medical Imaging and Visualization Centre (MMIV)University of Bergen Western Norway University of Applied Sciences image retrieval medical image processing source codes deep learning medical imaging magnetic resonance imaging

arXiv: 1811.10052 MMIV-ML/DLMI2018

Paper: A survey on deep learning in medical image analysis, Geert Litjens et al. (2017). This seminal 2017 survey thoroughly establishes the broad foundational landscape of deep learning applications across medical image analysis prior to this focused MRI review.
Paper: U-Net: Convolutional Networks for Biomedical Image Segmentation, Olaf Ronneberger et al. (2015). This paper introduces the U-Net architecture, which serves as the core foundational convolutional network for medical image segmentation and reconstruction across the MRI processing pipeline.
Paper: Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Konstantinos Kamnitsas et al. (2016). This work demonstrates seminal multi-scale 3D CNN architectures specifically tailored for volumetric brain lesion segmentation in multi-modal MRI.
Paper: Brain tumor segmentation with Deep Neural Networks, Mohammad Havaei et al. (2015). This foundational paper establishes early deep convolutional neural network approaches for automated brain tumor segmentation in multi-modal MRI scans.
Paper: Deep Convolutional Neural Network for Inverse Problems in Imaging, Kyong Hwan Jin et al. (2016). This work establishes the formulation of deep convolutional networks for solving imaging inverse problems and artifact reduction, directly informing deep learning acquisition and reconstruction pipelines.
Paper: Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?, Nima Tajbakhsh et al. (2016). This study analyzes transfer learning and fine-tuning strategies for convolutional networks under limited medical imaging data conditions.
Paper: Deep Residual Learning for Image Recognition, Kaiming He et al. (2016). This paper introduces residual networks (ResNets), establishing the primary deep backbone architecture leveraged across biomedical image processing tasks.
Paper: Machine learning for neuroimaging with scikit-learn, Alexandre Abraham et al. (2014). This paper provides essential background on standardized machine learning pipelines and neuroimaging data handling using functional MRI.

Paper: VoxelMorph: A Learning Framework for Deformable Medical Image Registration, Guha Balakrishnan et al. (2018). VoxelMorph extends deep learning in MRI beyond segmentation into rapid, unsupervised deformable volumetric image registration.
Paper: Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images, Ali Hatamizadeh et al. (2022). This work advances MRI brain tumor segmentation from standard convolutional methods to hierarchical 3D vision transformer architectures (Swin UNETR).
Paper: Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation, Hu Cao et al. (2021). Swin-Unet demonstrates pure vision transformer models for medical image segmentation, directly evaluating benchmarks including cardiac MRI.
Paper: The future of digital health with federated learning, Nicola Rieke et al. (2020). This article explores federated learning as a privacy-preserving framework to train multi-institutional deep learning models on distributed clinical MRI datasets.
Paper: A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges, M. Abdar et al. (2020). This survey provides an in-depth treatment of uncertainty quantification methods required to make deep medical imaging predictions clinically reliable.
Paper: UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation, Huimin Huang et al. (2020). This paper advances medical segmentation by introducing UNet 3+ with full-scale multi-level skip connections and deep supervision.

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Abstract

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.

Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

Topics

image retrieval medical image processing source codes convolutional neural networks deep belief networks correlation analysis natural language understanding online learning digital image processing

Keywords

deep learning medical imaging magnetic resonance imaging MRI reconstruction MRI segmentation MRI synthesis diffusion MRI functional MRI contrast-enhanced MRI multiparametric MRI

1 Introduction
2 Machine learning, artificial neural networks, deep learning
2.1 Artificial neural networks
2.2 Deep learning
2.3 Building blocks of convolutional neural networks
3 Deep learning, medical imaging and MRI
3.1 From image acquisition to image registration
3.1.1 Data acquisition and image reconstruction
3.1.2 Quantitative parameters - QSM and MR fingerprinting
3.1.3 Image restoration (denoising, artifact detection)
3.1.4 Image super-resolution
3.1.5 Image synthesis
3.1.6 Image registration
3.2 From image segmentation to diagnosis and prediction
3.2.1 Image segmentation
3.2.2 Diagnosis and prediction
3.3 Content-based image retrieval
4 Open science and reproducible research in machine learning for medical imaging
5 Challenges, limitations and future perspectives
5.1 Data
5.2 Interpretability, trust and safety
5.3 Workflow integration, regulations
5.4 Perspectives and future expectations
References

Knowls

Knowl 1 — Taxonomy of Deep Learning in the MRI Signal and Image Processing Chain
model/method
The application of deep learning across the magnetic resonance imaging (MRI) workflow spans the entire measurement and analysis pipeline, divided into physics-level signal processing and downstream clinical image analysis:
1. Data Acquisition and Image Reconstruction: Mapping complex-valued raw $k$ -space data to reconstructed spatial images. Approaches include physics-driven neural networks, automated transform by manifold approximation (AUTOMAP) formulating reconstruction as a data-driven supervised learning task from sensor to image domain, deep cascaded convolutional neural networks for dynamic MR reconstruction, variational networks for accelerated fast spin-echo acquisitions, and generative adversarial networks (such as GANCS and DAGAN) for high-fidelity compressed sensing reconstruction on the fly.
2. Quantitative Parameter Mapping: Inverting measured multi-echo or pseudo-randomized sequences to quantify intrinsic tissue parameters ( $T_1$ , $T_2$ , proton density, or susceptibility). This includes Quantitative Susceptibility Mapping (QSM) dipole inversion and Magnetic Resonance Fingerprinting (MRF) signal-to-parameter regression.
3. Image Restoration and Quality Enhancement: SVR and CNN-based image denoising, spatiotemporal filter models for dynamic contrast-enhanced (DCE) sequences, automated motion artifact detection, and reference-free ghosting correction.
4. Super-Resolution: Convolutional neural networks stacked with multi-scale fusion units to synthesize high-resolution isotropic volumes from anisotropic thin-slice acquisitions (e.g., DeepResolve).
5. Image Synthesis and Modality Translation: Generative adversarial frameworks (such as pix2pix-based GANs) translating MRI scans to synthetic pseudo-CT for PET/MR attenuation correction, generating synthetic pathological lesions (e.g., brain tumors or prostate lesions) for data augmentation, and producing inter-contrast translations.
6. Image Registration: Unsupervised and regression-based deformable 2D/3D registration networks (such as VoxelMorph and Quicksilver) optimizing spatial transformation displacement fields directly from image pairs without classical iterative numerical optimization.
7. Downstream Diagnostic Tasks: Anatomical segmentation (fully convolutional networks, U-Net, V-Net), disease classification, content-based image retrieval (CBIR), and automated natural language radiology report generation.
Knowl 2 — Deep Learning Methods for Quantitative Susceptibility Mapping and Magnetic Resonance Fingerprinting
model/method
Deep learning models serve as direct function approximators for ill-posed, computationally intensive inverse problems in quantitative MRI parameter estimation:
- Quantitative Susceptibility Mapping (QSM): Estimating spatial magnetic susceptibility distributions $\chi$ $χ$ from measured local magnetic field perturbations is an ill-posed inverse problem. Deep learning frameworks replace iterative multi-orientation conditioning (such as COSMOS) with convolutional neural networks:
  - QSMnet: A 3D U-Net trained on multi-orientation COSMOS datasets to produce high-quality susceptibility source maps from single-orientation phase data.
  - DeepQSM: A U-Net trained entirely on synthetically generated field perturbations from basic geometric shapes (cubes, rectangles, spheres) that generalizes successfully to clinical human brain data, computing susceptibility maps in seconds.
- Magnetic Resonance Fingerprinting (MRF): MRF utilizes pseudo-randomized pulse sequences to induce unique temporal signal trajectories for different tissue parameter combinations ( $T_1$ $T_{1}$ , $T_2$ $T_{2}$ , $M_0$ $M_{0}$ , $B_0$ $B_{0}$ ). Standard dictionary-matching based on Bloch equation simulations is computationally burdensome. Deep learning alternatives include:
  - MRF Deep Reconstruction Network (DRONE): A fully connected multi-layer perceptron mapping voxel temporal magnitude signals directly to quantitative $T_1$ and $T_2$ values. Training on discretized dictionary entries enables inference speeds 300 to 5,000 times faster than conventional inner-product dictionary matching.
  - Speech-Inspired 1D CNNs: 1D convolutional architectures processing long MRF time-series signals to extract temporal features for parameter regression.
  - Complex-Valued Feedforward Networks: Networks incorporating native complex activation functions (such as the complex cardioid) that operate directly on the real and imaginary components of complex RF signals rather than processing them as independent channels.
Knowl 3 — Medical Image Synthesis with Generative Adversarial Networks
model/method
Generative Adversarial Networks (GANs) pit a generative network $G$ against a discriminative network $D$ in a minimax game where $G$ creates realistic image distributions and $D$ differentiates synthetic from true images. In medical imaging, GANs address several operational challenges:
- Pathology and Lesion Synthesis for Data Augmentation: Conditional GAN architectures (e.g., pix2pix) learn to synthesize realistic pathological lesions—such as glioblastomas, ischemic stroke lesions, and prostate tumors—conditioned on anatomical background masks or healthy patient templates. Downstream segmentation and classification networks trained on GAN-augmented datasets achieve performance parity with networks trained on larger true clinical cohorts.
- Pseudo-Modality Synthesis: GANs synthesize CT images directly from MRI data (pseudo-CT generation) to compute attenuation correction maps for integrated PET/MRI scanners and radiotherapy planning, eliminating the need for separate ionizing CT scans.
- Anonymization and Domain Translation: Generative synthesis maps private clinical distributions into synthetic patient cohorts that preserve disease characteristics while removing identifying patient features.

Knowl 4 — Open-Source Deep Learning Software Platforms for Medical Imaging

data/table

Specialized open-source toolkits and model architectures designed for medical image processing are built on top of general deep learning frameworks (TensorFlow, PyTorch, Keras) and GPU-accelerated computing libraries (CUDA, cuDNN):

Platform / Architecture	Primary Purpose	Key Characteristics
NiftyNet	Medical image analysis	Modular CNN platform for segmentation, regression, and GANs
DLTK	Medical deep learning toolkit	TensorFlow-based reference models for biomedical imaging
DeepMedic	3D lesion segmentation	Multi-scale 3D CNN combined with fully connected CRFs
U-Net	2D biomedical segmentation	Fully convolutional encoder-decoder with skip connections
V-Net	3D volumetric segmentation	Volumetric convolutions, residual blocks, and Dice loss
SegNet	Semantic pixel labelling	Encoder-decoder retaining max-pooling indices for upsampling
VoxelMorph	3D image registration	Unsupervised end-to-end deformable image registration
Quicksilver	Fast image registration	Patch-wise prediction of LDDMM deformation models
AUTOMAP	Sensor-to-image reconstruction	Deep feedforward manifold approximation for $k$ -space data

These platforms provide modular building blocks and reproducible reference implementations for medical segmentation, reconstruction, registration, and synthesis pipelines.

Knowl 5 — Public Benchmark Repositories for Medical Imaging Machine Learning

data/table

Open-access imaging databases provide curated, large-scale cohorts essential for training, fine-tuning, and evaluating deep learning models across neuroimaging and oncology:

Repository	Focus Area	Cohort Scale and Imaging Modalities
OpenNeuro	Neuroimaging	Brain imaging from $>168$ studies ( $>4,718$ participants) across diverse modalities and acquisition protocols.
UK Biobank	Population health	Large-scale epidemiological cohort with multimodal MRI data ( $>15,000$ participants, scaling to $100,000$ ).
TCIA	Oncology	The Cancer Imaging Archive hosting public collections ( $>14,355$ cancer patients across 77 collections) in CT, MRI, and PET.
ABIDE	Neurodevelopment	Autism Brain Imaging Data Exchange with 1,114 datasets (521 Autism Spectrum Disorder subjects and 593 controls).
ADNI	Neurodegeneration	Alzheimer's Disease Neuroimaging Initiative with longitudinal MRI/PET data from $\sim 2,000$ subjects (controls, early/late MCI, AD).

These public datasets serve as standard benchmarks for transfer learning, cross-institutional validation, and methodological comparisons.

Knowl 6 — Benchmark Challenges in Medical Image Analysis

data/table

International scientific competitions serve as standardized benchmarks to measure algorithmic accuracy across segmentation, detection, and diagnostic classification tasks:

Challenge Name	Target Anatomical Domain	Primary Task
BraTS	Multimodal Brain MRI	Glioma sub-region semantic segmentation
ISLES	Brain CT Perfusion / MRI	Acute ischemic stroke lesion segmentation
PROSTATEx	Prostate mp-MRI ( $T_2$ , ADC, $K^{\text{trans}}$ )	Prostate lesion detection and malignancy classification
CAMELYON	Histological lymph node sections	Automated metastasis detection in whole-slide images
ISIC	Dermoscopic skin photography	Melanoma detection and skin lesion classification
RSNA Pneumonia	Chest radiographs	Pneumonia visual opacity detection and localization
HVSMR	Cardiovascular MRI (3D)	Blood pool and myocardium segmentation
MURA	Musculoskeletal X-rays	Upper extremity bone abnormality determination

Performance across these challenges has transitioned from traditional feature-engineering algorithms to the near-total dominance of deep convolutional architectures.

Knowl 7 — Data Scarcity, Generalization, and Privacy Bottlenecks in Medical Deep Learning
limitation
Deploying deep neural networks in clinical environments is constrained by data availability, distribution shifts, and data governance:
- Sample Inefficiency and Annotation Scarcity: Deep neural networks require thousands of expert-annotated samples. Practical mitigation strategies include:
  - Transfer Learning: Pre-training on large natural image datasets (ImageNet) for 2D tasks, or cross-organ transfer learning in 3D (e.g., pre-training on 3D brain segmentation before fine-tuning on kidney MRI to reduce required kidney annotations).
  - Data Augmentation: Label-preserving affine transformations, non-linear elastic deformations, registration-based temporal label propagation through dynamic image series, and generative synthesis (GANs).
- Distribution Shifts: Networks trained on curated, artifact-free research datasets often experience performance degradation when deployed on heterogeneous, noisy, or multi-vendor clinical data.
- Privacy and Model Inversion Vulnerabilities: Sharing trained model weights or hosting query APIs presents privacy risks, including membership inference and model-inversion attacks that reconstruct training samples. Distributed privacy-preserving paradigms—such as federated learning, split learning, and differential privacy—are necessary for multi-institutional training without transferring raw patient records.
Knowl 8 — Interpretability, Bayesian Uncertainty, and Clinical Safety in Deep Learning
limitation
The clinical deployment of deep neural networks requires addressing opacity, uncalibrated confidence, and operational safety:
- Interpretability and Black-Box Behavior: The complex hierarchical representations of deep neural networks prevent direct understanding of decision pathways. Methods such as saliency maps, gradient-weighted feature visualizations, and explainable AI toolkits are required to ensure predictions reflect pathological markers rather than confounding image acquisition artifacts.
- Bayesian Deep Learning and Uncertainty Estimation: Standard deterministic networks produce overconfident erroneous predictions on out-of-distribution or noisy inputs. Performing variational inference via Monte Carlo Dropout (maintaining active dropout sampling during the inference phase) produces calibrated predictive uncertainty estimates. These uncertainty maps highlight ambiguous tissue boundaries in segmentation and enhance robustness against adversarial perturbations.
- Clinical Workflow Integration: High algorithmic accuracy in isolation does not guarantee clinical efficacy. Systems must interface seamlessly with Picture Archiving and Communication Systems (PACS) and radiology workflows, requiring active feedback loops with clinicians, prospective validation, and navigation of medical device regulatory approvals.
Knowl 9 — Architectural Components of Convolutional Neural Networks for Image Analysis
model/method
Convolutional Neural Networks (CNNs) preserve spatial relationships by composing specialized computational operations:
1. Convolutional Layers and Weight Sharing: The layer activations $f_k$ are produced by convolving the input activations with parameterized kernel filter tensors $W^{(j, i)}$ of spatial size (typically $3 \times 3$ or $3 \times 3 \times 3$ ). Weight sharing enforces translational equivariance and drastically reduces parameter count compared to dense feedforward layers.
2. Nonlinear Activation Functions: Nonlinear operations applied element-wise across feature maps enable universal function approximation. Standard functions include the Rectified Linear Unit: $\text{ReLU}(z) = \max(0, z)$ as well as Leaky ReLU, Parametric ReLU (PReLU), and Exponential Linear Units (ELU).
3. Pooling and Strided Convolutions: Spatial downsampling via max-pooling, average pooling, or increased convolution stride lengths enlarges the receptive field and provides local translational invariance.
4. Regularization and Normalization:
  - Dropout: Stochastically sets a fraction of neuron activations to zero during mini-batch training to reduce co-adaptation and perform implicit ensemble averaging.
  - Batch Normalization: Normalizes layer inputs across training batches to zero mean and unit variance, stabilizing internal covariate shift and speeding up convergence.
5. Residual and Skip Connections: In architectures such as ResNet, Highway Networks, U-Net, and DenseNet, identity or concatenated skip connections allow gradients to flow unimpeded through very deep networks (e.g., $\ge 100$ layers), facilitating multi-scale feature aggregation and residual learning.
Knowl 10 — Organ-Specific Diagnostic and Predictive MRI Applications
empirical result
Deep neural networks achieve high diagnostic accuracy across specialized organ-specific MRI tasks:
- Brain MRI:
  - Structural and Functional Connectomes: 3D CNNs directly segment white matter fiber tracts from fiber orientation distribution function (fODF) peaks (e.g., TractSeg) without tractography or registration. Transfer learning on functional connectivity matrices enables multi-site schizophrenia classification.
  - Neurodegeneration and Aging: Predicting chronological brain age from structural $T_1$ -weighted MRI yields a heritable imaging biomarker for neurodegenerative decline; multi-instance learning and multimodal networks (MRI + FDG-PET) classify stages of Alzheimer's disease.
  - Neuro-Oncology: Cascaded multi-scale 3D CNNs and holistically nested networks segment glioblastoma multiforme sub-compartments and meningiomas from clinical routine multiparametric MRI.
- Prostate MRI: Automated prostate boundary segmentation combines fully convolutional networks with edge detection; multi-stream CNNs operating in parallel on $T_2$ -weighted and Apparent Diffusion Coefficient (ADC) maps differentiate prostate cancer (PCa) from prostatitis and benign prostatic hyperplasia.
- Kidney MRI: Fully automated volumetric segmentation of polycystic kidneys using multi-observer ensemble networks, and detection of acute renal transplant rejection from diffusion-weighted MRI using stacked constrained autoencoders.
- Spine MRI: Automated vertebrae detection and labeling, 3D multi-scale intervertebral disc segmentation, DeepSPINE for lumbar stenosis grading, and multitask networks for lumbar neural foraminal stenosis diagnosis.

Coverage note — Generic introductory deep learning history and introductory coding tutorials were omitted as they constitute standard background rather than synthesized domain contributions.

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MLA

Lundervold, A. S., and A. Lundervold. “An Overview of Deep Learning in Medical Imaging Focusing on MRI”. Zeitschrift Für Medizinische Physik, vol. 29, no. 2, 2019, pp. 102–27, https://doi.org/10.1016/j.zemedi.2018.11.002.

APA

Lundervold, A. S., & Lundervold, A. (2019). An overview of deep learning in medical imaging focusing on MRI. Zeitschrift Für Medizinische Physik, 29(2), 102–127. https://doi.org/10.1016/j.zemedi.2018.11.002

Chicago

Lundervold, A. S., and A. Lundervold. 2019. “An Overview of Deep Learning in Medical Imaging Focusing on MRI”. Zeitschrift Für Medizinische Physik 29 (2): 102–27. https://doi.org/10.1016/j.zemedi.2018.11.002.

Harvard

Lundervold, A.S. and Lundervold, A. (2019) “An overview of deep learning in medical imaging focusing on MRI”, Zeitschrift für Medizinische Physik, 29(2), pp. 102–127. Available at: https://doi.org/10.1016/j.zemedi.2018.11.002.

Vancouver

1. Lundervold AS, Lundervold A (2019) An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik 29:102–127

BibTeX

@article{Lundervold_2019, title={An overview of deep learning in medical imaging focusing on MRI}, volume={29}, ISSN={0939-3889}, url={http://dx.doi.org/10.1016/j.zemedi.2018.11.002}, DOI={10.1016/j.zemedi.2018.11.002}, number={2}, journal={Zeitschrift für Medizinische Physik}, publisher={Elsevier BV}, author={Lundervold, Alexander Selvikvåg and Lundervold, Arvid}, year={2019}, month=May, pages={102–127} }

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An overview of deep learning in medical imaging focusing on MRI

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Knowls

Knowl 1 — Taxonomy of Deep Learning in the MRI Signal and Image Processing Chain

Knowl 2 — Deep Learning Methods for Quantitative Susceptibility Mapping and Magnetic Resonance Fingerprinting

Knowl 3 — Medical Image Synthesis with Generative Adversarial Networks

Knowl 4 — Open-Source Deep Learning Software Platforms for Medical Imaging

Knowl 5 — Public Benchmark Repositories for Medical Imaging Machine Learning

Knowl 6 — Benchmark Challenges in Medical Image Analysis

Knowl 7 — Data Scarcity, Generalization, and Privacy Bottlenecks in Medical Deep Learning

Knowl 8 — Interpretability, Bayesian Uncertainty, and Clinical Safety in Deep Learning

Knowl 9 — Architectural Components of Convolutional Neural Networks for Image Analysis

Knowl 10 — Organ-Specific Diagnostic and Predictive MRI Applications

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