End-to-end deep learning patient level classification of affected territory of ischemic stroke patients in DW-MRI

Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW et al (2021) Heart disease and stroke statistics: 2021 update: a report from the American Heart Association. Circulation 143:e254e743. https://doi.org/10.1161/CIR.0000000000000950

Article  Google Scholar 

van Leeuwen KG, Schalekamp S, Rutten MJCM et al (2021) Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol 31:3797–3804. https://doi.org/10.1007/s00330-021-07892-z

Article  PubMed  PubMed Central  Google Scholar 

Baron CA, Kate M, Gioia L et al (2015) Reduction of diffusion-weighted imaging contrast of acute ischemic stroke at short diffusion times. Stroke 46(8):2136–2141. https://doi.org/10.1161/STROKEAHA.115.008815

Article  PubMed  Google Scholar 

Cetinoglu YK, Koska IO, Uluc ME, Gelal MF Detection and vascular territorial classification of stroke on diffusion-weighted MRI by deep learning. Eur J Radiol 145: 110050. https://doi.org/10.1016/j.ejrad.2021.110050

Lee KY, Liu CC, Chen DYT et al (2023) Automatic detection and vascular territory classification of hyperacute staged ischemic stroke on diffusion weighted image using convolutional neural networks. Sci Rep 13:404. https://doi.org/10.1038/s41598-023-27621-4

Article  CAS  PubMed  PubMed Central  Google Scholar 

Koska IO, Selver MA, Gelal F et al (2024) Voxel level dense prediction of acute stroke territory in DWI using deep learning segmentation models and image enhancement strategies. Japanese J Radiol. https://doi.org/10.1007/s11604-024-01582-8

Article  Google Scholar 

Candemir S, Nguyen XV, Folio LR, Prevedello LM (2021) Training strategies for Radiology Deep Learning models in Data-limited scenarios. Radiol Artif Intell 3(6):e210014. https://doi.org/10.1148/ryai.2021210014

Article  PubMed  PubMed Central  Google Scholar 

Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29(6):1310–1320. https://doi.org/10.1109/TMI.2010.2046908

Article  PubMed  PubMed Central  Google Scholar 

Garyfallidis E, Brett M, Amirbekian B, Rokem A, van der Walt S, Descoteaux M, Nimmo-Smith I, Dipy, Contributors (2014) DIPY, a library fort he analysis of diffusion MRI data. Frontiers in Neuroinformatics, 2014;8:8. https://doi.org/10.3389/fninf.2014.00008

Reinhold JC, Dewey BE, Carass A, Prince JL (2019) Evaluating the impact of intensity normalization on MR Image Synthesis. Proc SPIE Int Soc Opt Eng 10949:109493H. https://doi.org/10.1117/12.2513089

Article  PubMed  PubMed Central  Google Scholar 

Mazziotta JC, Toga AW, Evans A, Fox P, Lancaster J (1995) A probabilistic atlas of the human brain: theory and rationale for its development. Int Consortium Brain Mapp (ICBM) Neuroimage 2(2):89–101. https://doi.org/10.1006/nimg.1995.1012

Article  CAS  Google Scholar 

Cheng PM, Montagnon E, Yamashita R, Pan I, Cadrin-Chênevert A, Perdigón Romero F, Chartrand G, Kadoury S, Tang A (2021) Deep learning: an update for radiologists. Radiographics 41(5):1427–1445. https://doi.org/10.1148/rg.2021200210

Article  PubMed  Google Scholar 

Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf H (1998) Support vector machines. IEEE Intell Syst Their Appl 13:18–28. https://doi.org/10.1109/5254.708428

Article  Google Scholar 

Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

Article  Google Scholar 

Schapire RE (2013) Explaining adaboost. Empirical inference. Springer, pp 37–52. https://doi.org/10.1007/978-3-642-41136-6_5

Li W (2014) Gaussian Process Learning: A Divide and Conquer Approach. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science vol 8866. Springer. https://doi.org/10.1007/978-3-319-12436-0_29

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

Article  CAS  PubMed  Google Scholar 

Soffer S, Ben-Cohen A, Shimon O, Amitai MM, Greenspan H, Klang E (2019) Convolutional neural networks for radiologic images: a radiologist’s guide. Radiology 290(3):590–606. https://doi.org/10.1148/radiol.2018180547

Article  PubMed  Google Scholar 

Ba J, Mnih V, Kavukcuoglu K Multiple object recognition with visual attention. arXiv preprint arXiv:1412.7755, 2014. https://doi.org/10.48550/arXiv.1412.7755

Zhai J, Zhang S, Chen J, He Q, Autoencoder, Its Various V (2018) IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, 2018, pp. 415–419. https://doi.org/10.1109/SMC.2018.00080

Gallant SI (1990) June. Perceptron-based learning algorithms, in IEEE Transactions on Neural Networks, vol. 1, no. 2, pp. 179–191, https://doi.org/10.1109/72.80230

Woo S, Park J, Lee J, Kweon I (2018) CBAM: Convolutional Block Attention Module. ArXiv, abs/1807.06521. https://doi.org/10.48550/arXiv.1807.06521

Chollet F, Keras (2015) Software available from keras.io

Haq AUH, IIMFCBM: Intelligent integrated model for feature extraction and classification of brain tumors using MRI clinical imaging data in IoT-healthcare. IEEE J Biomed Health Inf. https://doi.org/10.1109/JBHI.2022.3171663

Montaha S, Azam S, Rafid AKMRH, Hasan MZ, Karim A, Islam A, Time Distributed CNN-LSTM (2022) A Hybrid Approach Combining CNN and LSTM to Classify Brain Tumor on 3D MRI Scans Performing Ablation Study, in IEEE Access, vol. 10, pp. 60039–60059, https://doi.org/10.1109/ACCESS.2022.3179577

Jamaludin A, Kadir T, Zisserman A, SpineNet (2017) Automated classification and evidence visualization in spinal MRIs. Med Image Anal 41:63–73. https://doi.org/10.1016/j.media.2017.07.002

Liu L, Kurgan L, Wu FX, Wang J (2020) Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease. Med Image Anal 65:101791. https://doi.org/10.1016/j.media.2020.101791

Article  PubMed  Google Scholar 

Li Z, Xing Q, Li Y et al A Novel Multi-Scale Channel Attention-Guided Neural Network for Brain Stroke Lesion Segmentation, in IEEE Access, vol. 11, pp. 66050–66062. https://doi.org/10.1109/ACCESS.2023.3289909

Hussein R, Zhao M, Shin D et al (2022) Multi-task Deep Learning for Cerebrovascular Disease Classification and MRI-to-PET Translation. 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada, 2022, pp. 4306–4312. https://doi.org/10.48550/arXiv.2202.06142

Xie X, Niu J, Liu X, Chen Z, Tang S, Yu S (2020) A survey on incorporating domain knowledge into deep learning for medical image analysis. Med Image Anal 69:101985. https://doi.org/10.1016/j.media.2021.101985

Article  Google Scholar 

Tao WD, Liu M, Fisher M et al (2012) Posterior versus anterior circulation infarction: How different are the neurological deficits? Stroke.;43(8): 2060–2065. https://doi.org/10.1161/STROKEAHA.112.652420

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