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Cancer has relational information residing at varying scales, modalities, and resolutions of the acquired data, such as radiology, pathology, genomics, proteomics, and clinical records.
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S. Ahmed, D. Dera, S. U. Hassan, N. Bouaynaya, and G. Rasool, “Failure detection in deep neural networks for medical imaging,” Frontiers in Medical Technology , vol. 4, 2022
2022
Later among the works it cites.
R. L. Siegel, K. D. Miller, N. S. Wagle, and A. Jemal, “Cancer Statistics, 2023,” CA: A Cancer Journal for Clinicians , vol. 73, no. 1, pp. 17–48, 2023
2023
Closest in time.
M. Çalışkan and K. Tazaki, “Ai/ml advances in non-small cell lung cancer biomarker discovery,” Frontiers in Oncology , vol. 13, 2023
2023
Closest in time.
B. Chen, J. Jin, H. Liu, Z. Yang, H. Zhu, Y. Wang, J. Lin, S. Wang, and S. Chen, “Trends and hotspots in research on medical images with deep learning: a bibliometric analysis from 2013 to 2023,” Frontiers in Artificial Intelligence , vol. 6, p. 1289669, 2023
2023
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A. Siam, A. R. Alsaify, B. Mohammad, M. R. Biswas, H. Ali, and Z. Shah, “Multimodal deep learning for liver cancer applications: a scoping review,” Frontiers in Artificial Intelligence , vol. 6, 2023
2023
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S. Khan, H. Ali, and Z. Shah, “Identifying the role of vision transformer for skin cancer—a scoping review,” Frontiers in Artificial Intelligence , vol. 6, Jul. 2023. [Online]. Available: http://dx.doi.org/10.3389/frai.2023.1202990
2023
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L. J. Muhammad and A. Bria, “Editorial: Ai applications for diagnosis of breast cancer,” Frontiers in Artificial Intelligence , vol. 6, Oct. 2023. [Online]. Available: http://dx.doi.org/10.3389/frai.2023.1247261
2023
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Y. Barhoumi, N. C. Bouaynaya, and G. Rasool, “Efficient scopeformer: Towards scalable and rich feature extraction for intracranial hemorrhage detection,” IEEE Access , 2023
2023
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N. Ghaffari Laleh, M. Ligero, R. Perez-Lopez, and J. N. Kather, “Facts and hopes on the use of artificial intelligence for predictive immunotherapy biomarkers in cancer,” Clinical Cancer Research , vol. 29, no. 2, pp. 316–323, 2023
2023
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2023
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P. Xu, X. Zhu, and D. A. Clifton, “Multimodal learning with transformers: A survey,” IEEE Transactions on Pattern Analysis &; Machine Intelligence , vol. 45, no. 10, pp. 12 113–12 132, oct 2023
2023
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Y. Ektefaie, G. Dasoulas, A. Noori, M. Farhat, and M. Zitnik, “Multimodal learning with graphs,” Nature Machine Intelligence , pp. 1–11, 2023
2023
Closest in time.
A. Waqas, M. M. Bui, E. F. Glassy, I. El Naqa, P. Borkowski, A. A. Borkowski, and G. Rasool, “Revolutionizing digital pathology with the power of generative artificial intelligence and foundation models,” Laboratory Investigation , p. 100255, 2023
2023
Closest in time.
2023
Closest in time.
M. Tortora, E. Cordelli, R. Sicilia, L. Nibid, E. Ippolito, G. Perrone, S. Ramella, and P. Soda, “Radiopathomics: Multimodal learning in non-small cell lung cancer for adaptive radiotherapy,” IEEE Access , 2023
2023
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2023
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K. Han, Y. Wang, H. Chen, X. Chen, J. Guo, Z. Liu, Y. Tang, A. Xiao, C. Xu, Y. Xu, Z. Yang, Y. Zhang, and D. Tao, “A Survey on Vision Transformer,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 1, pp. 87–110, 2023
2023
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Z. Zhong, D. Schneider, M. Voit, R. Stiefelhagen, and J. Beyerer, “Anticipative Feature Fusion Transformer for Multi-Modal Action Anticipation,” in 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , 2023, pp. 6057–6066
2023
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2023
Closest in time.
M. Zhao, X. Huang, J. Jiang, L. Mou, D.-M. Yan, and L. Ma, “Accurate Registration of Cross-Modality Geometry via Consistent Clustering,” IEEE Transactions on Visualization and Computer Graphics , 2023
2023
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W. Muhammad, S.-b.-S. Ahmed, S. Naeem, A. A. M. H. Khan, B. M. Qureshi, A. Hussain, and B. Aydogan, “Artificial neural network-assisted prediction of radiobiological indices in head and neck cancer,” Frontiers in Artificial Intelligence , vol. 7, p. 1329737, 2024
2024
Closest in time.
R. Talebi, C. A. Celis-Morales, A. Akbari, A. Talebi, N. Borumandnia, and M. A. Pourhoseingholi, “Machine learning-based classifiers to predict metastasis in colorectal cancer patients,” Frontiers in Artificial Intelligence , vol. 7, 2024
2024
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E. V. Varlamova, M. A. Butakova, V. V. Semyonova, S. A. Soldatov, A. V. Poltavskiy, O. I. Kit, and A. V. Soldatov, “Machine learning meets cancer,” Cancers , vol. 16, no. 6, p. 1100, 2024
2024
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A. Tripathi, A. Waqas, K. Venkatesan, Y. Yilmaz, and G. Rasool, “Building flexible, scalable, and machine learning-ready multimodal oncology datasets,” Sensors , vol. 24, no. 5, p. 1634, 2024
2024
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A. Tripathi, A. Waqas, Y. Yilmaz, and G. Rasool, “Multimodal transformer model improves survival prediction in lung cancer compared to unimodal approaches,” Cancer Research , vol. 84, no. 6_Supplement, pp. 4905–4905, 2024
2024
Closest in time.
2024
Closest in time.
A. Waqas, A. Tripathi, S. Ahmed, A. Mukund, P. Stewart, M. Naeini, H. Farooq, and G. Rasool, “Senmo: A self-normalizing deep learning model for enhanced multi-omics data analysis in oncology,” Cancer Research , vol. 84, no. 6_Supplement, pp. 908–908, 2024
2024
Closest in time.
C. V. Gonzalez Zelaya, “Towards explaining the effects of data preprocessing on machine learning,” in IEEE 35th International Conference on Data Engineering (ICDE) , 2019, pp. 2086–2090
2090
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