Review of Graph Neural Networks for Medical Image

Jing Wang

doi:10.4108/eetel.4358

Authors

Jing Wang Henan Polytechnic University

DOI:

https://doi.org/10.4108/eetel.4358

Keywords:

graph neural networks, deep learning, graph data, medical images, denoising

Abstract

As deep learning continues to evolve, more and more applications are generating data from non-Euclidean domains and representing them as graphs with complex relationships and interdependencies between objects. This poses a significant challenge to deep learning algorithms. Because, due to the uniqueness of graphs, applying deep learning to ubiquitous graph data is not an easy task. To solve the problem in non-Euclidean domains, graph Neural Networks (GNNs) have emerged. A graph neural network (GNN) is a neural model that captures dependencies between graphs by passing messages between graph nodes. With the continuous development of medical image technology, medical image diagnosis plays a crucial role in clinical practice. However, in practice, medical images are often affected by noise, artifacts, and other interfering factors, which may lead to inaccurate and unstable diagnostic results. Therefore, image-denoising techniques become especially critical in medical image processing. Therefore, researchers have proposed innovative methods based on graph neural networks for effective noise removal, preserving the key features of the image and improving the quality and usability of medical images. This paper reviews the research progress of graph neural networks in the field of medical image denoising. It also summarises the problems and challenges of current research and looks at the future direction of medical image-denoising research.

References

Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-44, May 28 2015.

Y. Zhang, "Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization," Simulation, vol. 92, no. 9, pp. 873-885, September 12, 2016 2016.

S. Wang, "Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy," Entropy, vol. 17, no. 12, pp. 8278-8296, 2015.

I. Nedjar, M. Brahimi, S. Mahmoudi, K. A. Ayad, and A. Chikh, "Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning," ArXiv, vol. abs/2305.20058, 2023.

O. Koepueklue, S. Hoermann, F. Herzog, H. Cevikalp, and G. Rigoll, "Dissected 3D CNNs: Temporal skip connections for efficient online video processing," Computer vision and image understanding: CVIU, p. 215, 2022.

A. Amrouche and J. Rouvaen, "On the Use of the Nonparametric Regression in Neural Network Based Approach Applied to Arabic Speech Recognition," 2022.

W. Ceusters, "Medical Natural Language Understanding as a Supporting Technology for Data Mining in Healthcare," 2022.

L. Butcher, "Big data," Engine + Powertrain Technology International, no. Jan., 2022.

M. M. Al-Qutt, H. Khaled, and R. E. Gohary, "Neural Network Inversion-Based Model for Predicting an Optimal Hardware Configuration: Solving Computationally Intensive Problems," International journal of grid and high performance computing, no. 2, p. 13, 2021.

Z. Alavi, S. Sharma, L. Zhou, and K. Chen, "Scalable Euclidean Embedding for Big Data," in IEEE International Congresson Big Data;IEEE Asia‐Pacific Conference on Services Computing;IEEE International conference on cloud computing;IEEE World Congress on Services.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.

L. Backstrom and J. Leskovec, "Supervised Random Walks: Predicting and Recommending Links in Social Networks," in Web Search and Data Mining, 2010.

M. Curado, L. Tortosa, and J. F. Vicent, "Deep learning model of convolutional neural networks powered by a genetic algorithm for prevention of traffic accidents severity," 2023.

J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, "Neural Message Passing for Quantum Chemistry," 2017.

S. Wu, Y. Tang, Y. Zhu, L. Wang, and T. Tan, "Session-Based Recommendation with Graph Neural Networks," 2019.

Michael et al., "Geometric Deep Learning: Going beyond Euclidean data," IEEE Signal Processing Magazine, vol. 34, no. 4, pp. 18-42, 2017.

J. M. Thomas, A. Moallemy-Oureh, S. Beddar-Wiesing, and C. Holzhüter, "Graph Neural Networks Designed for Different Graph Types: A Survey," pp. 200-212, 2022.

K. Muhammad, "Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation," Multimedia Tools and Applications, vol. 78, no. 3, pp. 3613-3632, 2019.

Z. Cui, K. Henrickson, R. Ke, and Y. Wang, "Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting," IEEE, no. 11, pp. 4883-4894, 2020.

A. Sperduti and A. Starita, "Supervised neural networks for the classification of structures," IEEE Transactions on Neural Networks, vol. 8, no. 3, p. 714, 1997.

M. Gori, G. Monfardini, and F. Scarselli, "A new model for learning in graph domains," in IEEE International Joint Conference on Neural Networks, 2005.

F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, "The Graph Neural Network Model," IEEE Transactions on Neural Networks, vol. 20, no. 1, p. 61, 2009.

C. Gallicchio and A. Micheli, "Graph Echo State Networks," in International Joint Conference on Neural Networks, 2010.

Y. L. Xu, K. Konstantinidis, and D. P. Mandic, "Tensor Networks for Multi-Modal Non-Euclidean Data," 2021.

Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, "A Comprehensive Survey on Graph Neural Networks," IEEE transactions on neural networks and learning systems, no. 1, p. 32, 2021.

Z. Zhang, P. Cui, and W. Zhu, "Deep Learning on Graphs: A Survey," 2018.

L. Waikhom and R. Patgiri, "Graph Neural Networks: Methods, Applications, and Opportunities," 2021.

J. Zhou et al., "Graph neural networks: A review of methods and applications," AI Open, vol. 1, pp. 57-81, 2020.

Z. Cao, X. Li, and L. Zhao, "Unsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral Classification," 2020.

L. Yang, J. Gu, C. Wang, X. Cao, and Y. Guo, "Toward Unsupervised Graph Neural Network: Interactive Clustering and Embedding via Optimal Transport," in 2020 IEEE International Conference on Data Mining (ICDM), 2020.

M. Okuda, S. I. Satoh, Y. Sato, and Y. Kidawara, "Unsupervised Common Particular Object Discovery and Localization by Analyzing a Match Graph," in IEEE International Conference on Acoustics, Speech and Signal Processing, 2021.

S.-H. Wang, "Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network," Information Fusion, vol. 67, pp. 208-229, 2020/10/09/ 2021.

D. S. Guttery, "Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network," Information Processing and Management, vol. 58, 2, 2021, Art no. 102439.

N. Takahashi, T. Yamakawa, Y. Minetoma, T. Nishi, and T. Migita, "Design of continuous-time recurrent neural networks with piecewise-linear activation function for generation of prescribed sequences of bipolar vectors," Neural Networks: The Official Journal of the International Neural Network Society, 2023.

Y.-D. Zhang, "Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU," Journal of Computational Science, vol. 28, pp. 1-10, 2018/09/01/ 2018.

A. K. Sangaiah, "Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization," Neural Computing and Applications, vol. 32, pp. 665-680, 2020.

C. Tang, C. Hu, J. Sun, S. H. Wang, and Y. D. Zhang, "NSCGCN: A novel deep GCN model to diagnosis COVID-19," Comput Biol Med, vol. 150, p. 106151, Sep 30 2022.

F. M. Bianchi, D. Grattarola, L. Livi, and C. Alippi, "Graph Neural Networks with Convolutional ARMA Filters," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PP, no. 99, pp. 1-1, 2021.

J. X. A. B et al., "Attention adjacency matrix based graph convolutional networks for skeleton-based action recognition - ScienceDirect," Neurocomputing, vol. 440, pp. 230-239, 2021.

P. Velikovi, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, "Graph Attention Networks," 2017.

S. H. Wang and Y. D. Lv, "Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling," Journal of Medical Systems, vol. 42, no. 1, Jan 2018, Art no. 2.

Sato, Yoshinobu, Nakamoto, and Masahiko, "Image Guidance of Breast Cancer Surgery Using 3-D Ultrasound Images and Augmented Reality," IEEE Transactions on Medical Imaging, 1998.

V. D. S. Rogier, N. Bhaskhar, D. Rubin, C. Langlotz, and A. Chaudhari, "Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays," 2023.

V. T. N. Pham, Q. C. Nguyen, and Q. V. Nguyen, "Chest X-Rays Abnormalities Localization and Classification Using an Ensemble Framework of Deep Convolutional Neural Networks," Vietnam Journal of Computer Science, vol. 10, no. 01, pp. 55-73, 2023.

Katja et al., "Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters," NeuroImage, vol. 50, no. 3, pp. 883-892, 2010.

K. E. Watkins et al., "Structural Asymmetries in the Human Brain: a Voxel-based Statistical Analysis of 142 MRI Scans," Cerebral Cortex, no. 9, pp. 868-877.

P. Arora, P. Singh, A. Girdhar, and R. Vijayvergiya, "Performance analysis of various denoising filters on intravascular ultrasound coronary artery images," International Journal of Imaging Systems and Technology, vol. 33, no. 3, pp. 965-984, 2023.

S. Y. Zhang et al., "Hformer:highly efficient vision transformer for low-dose CT denoising," vol. 34, no. 4, pp. 161-174, 2023.

H. Chi, Y. Wang, Q. Hao, and H. Xia, "Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks," JPhCS, 2022.

E. P. Barracchia, G. Pio, A. Bifet, H. M. Gomes, B. Pfahringer, and M. Ceci, "LP-ROBIN: Link prediction in dynamic networks exploiting incremental node embedding," Information Sciences: An International Journal, p. 606, 2022.

Y. Zhu, J. Wang, K. Zhang, and J. Zhang, "Node Embedding and Classification with Adaptive Structural Fingerprint," Neurocomputing, vol. 502, pp. 196-208, 2022.

Y. D. Zhang and S. Satapathy, "A seven-layer convolutional neural network for chest CT-based COVID-19 diagnosis using stochastic pooling," IEEE Sens. J., vol. 22, no. 18, pp. 17573 - 17582, 2022.

T. N. Kipf and M. Welling, "Semi-Supervised Classification with Graph Convolutional Networks," 2016.

X. Liu, M. Qin, Y. He, X. Mi, and C. Yu, "A new multi-data-driven spatiotemporal PM2.5 forecasting model based on an ensemble graph reinforcement learning convolutional network," Atmospheric Pollution Research, pp. 274-320, 2021.