1. Kokubo, Y.; Matsumoto, C. Hypertension Is a Risk Factor for Several Types of Heart Disease: Review of Prospective Studies. Hypertens. Basic Res. Clin. Pract. Adv. Exp. Med. Biol. 2017, 956, 419–426. [CrossRef]
2. Wexler, R.; Elton, T.; Pleister, A.; Feldman, D. Cardiomyopathy: An Overview. Am. Fam. Phys. 2009, 79, 778–784.
3. Chugh, S.S. Early Identification of Risk Factors for Sudden Cardiac Death. Nat. Rev. Cardiol. 2010, 7, 318–326. [CrossRef] [PubMed]
4. Clough, J.R.; Oksuz, I.; Puyol-Antón, E.; Ruijsink, B.; King, A.P.; Schnabel, J.A. Global and Local Interpretability for Cardiac MRI Classification. Lect. Notes Comput. Sci. (Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform.) 2019, 11767 LNCS, 656–664. [CrossRef]
5. Ammar, A.; Bouattane, O.; Youssfi, M. Automatic Cardiac Cine MRI Segmentation and Heart Disease Classification. Comput. Med. Imaging Graph. 2021, 88, 101864. [CrossRef]
6. Tong, Q.; Li, C.; Si, W.; Liao, X.; Tong, Y.; Yuan, Z.; Heng, P.A. RIANet: Recurrent Interleaved Attention Network for Cardiac MRI Segmentation. Comput. Biol. Med. 2019, 109, 290–302. [CrossRef]
7. Ma, Y.; Wang, L.; Ma, Y.; Dong, M.; Du, S.; Sun, X. An SPCNN-GVF-Based Approach for the Automatic Segmentation of Left Ventricle in Cardiac Cine MR Images. Int. J. Comput. Assist. Radiol. Surg. 2016, 11, 242–255. [CrossRef]
8. Isensee, F.; Jaeger, P.F.; Full, P.M.; Wolf, I.; Engelhardt, S.; Maier-Hein, K.H. Automatic Cardiac Disease Assessment on Cine-MRI via Time-Series Segmentation and Domain Specific Features. Lect. Notes Comput. Sci. (Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform.) 2018, 10663 LNCS, 120–129. [CrossRef]
9. Khened, M.; Kollerathu, V.A.; Krishnamurthi, G. Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis Using Ensemble of Classifiers. Med. Image Anal. 2019, 51, 21–45. [CrossRef]
10. Wolterink, J.M.; Leiner, T.; Viergever, M.A.; Išgum, I. Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images. Lect. Notes Comput. Sci. (Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform.) 2018, 10663 LNCS, 101–110. [CrossRef]
11. Khened, M.; Alex, V.; Krishnamurthi, G. Densely Connected Fully Convolutional Network for Short-Axis Cardiac Cine MR Image Segmentation and Heart Diagnosis Using Random Forest. Lect. Notes Comput. Sci. (Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform.) 2018, 10663 LNCS, 140–151. [CrossRef]
12. Vinyals, O.; Blundell, C.; Lillicrap, T.; Kavukcuoglu, K.; Wierstra, D. Matching Networks for One Shot Learning. Adv. Neural Inf. Process. Syst. 2016, 3637–3645.
13. Hu, J.; Lu, J.; Tan, Y.-P.; Zhou, J. Deep Transfer Metric Learning. IEEE Trans. Image Process. 2016, 25, 5576–5588. [CrossRef] [PubMed]
14. Jake, S.; Kevin, S.; Richard, Z. Prototypical Networks for Few-Shot Learning. Adv. Neural Inf. Process. Syst. 2017, 30, 4077–4087.
15. Simon, C.; Koniusz, P.; Nock, R.; Harandi, M. Adaptive Subspaces for Few-Shot Learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 14–19 June 2020; pp. 4135–4144.
16. Wang, Y. Low-Shot Learning from Imaginary Data. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA, 18–22 June 2018; pp. 7278–7286.
17. Finn, C.; Abbeel, P.; Levine, S. Model-Agnostic Meta-learning for Fast Adaptation of Deep Networks. In Proceedings of the 34th International Conference Machinability Learned ICML, Sydney, Australia, 6–11 August 2017; Volume 3, pp. 1856–1868.
18. Li, X.; Yang, X.; Ma, Z.; Xue, J.-H. Deep Metric Learning for Few-Shot Image Classification: A Selective Review. arXiv 2021, arXiv:2105.08149.
19. Chen, W.; Wang, Y.F.; Liu, Y.; Kira, Z.; Tech, V. A Closer Look at Few-Shot Classification. international conference Learned Representacion. arXiv 2019, arXiv:1904.04232.
20. Dvornik, N.; Mairal, J.; Schmid, C. Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. Proc. IEEE Int. Conf. Comput. Vis. 2019, 2019, 3722–3730.
21. Bernard, O.; Lalande, A.; Zotti, C.; Cervenansky, F.; Yang, X.; Heng, P.A.; Cetin, I.; Lekadir, K.; Camara, O.; Gonzalez Ballester, M.A.; et al. Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved? IEEE Trans. Med. Imaging 2018, 37, 2514–2525. [CrossRef]
22. Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; Chen, L.-C. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA, 18–22 June 2018; pp. 4510–4520.
23. Tan, M.; Le, Q.V. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In Proceedings of the 36th International Conference Machinability Learned ICML, Long Beach, CA, USA, 9–15 June 2019; Volume 2019, pp. 10691–10700.
24. Howard, A.; Wang, W.; Chu, G.; Chen, L.; Chen, B.; Tan, M. Searching for MobileNetV3. In Proceedings of the international conference Computability Vision, Seoul, Korea, 27 October–2 November 2019; pp. 1314–1324.
25. Wibowo, A.; Pratama, C.; Sahara, D.P.; Heliani, L.S.; Rasyid, S.; Akbar, Z.; Muttaqy, F.; Sudrajat, A. Earthquake Early Warning System Using Ncheck and Hard-Shared Orthogonal Multitarget Regression on Deep Learning. IEEE Geosci. Remote Sens. Lett. 2022, 19, 1–5. [CrossRef]
26. Wibowo, A.; Purnama, S.R.; Wirawan, P.W.; Rasyidi, H. Lightweight Encoder-Decoder Model for Automatic Skin Lesion Segmentation. Inform. Med. Unlocked. 2021, 25, 100640. [CrossRef]
27. Ronneberger, O.; Fischer, P.; Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. Lect. Notes Comput. Sci. (Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform.) 2015, 9351, 234–241. [CrossRef]
28. Keskar, N.S.; Nocedal, J.; Tang, P.T.P.; Mudigere, D.; Smelyanskiy, M. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. In Proceedings of the 5th International Conference Learned Representacion ICLR, Toulon, France, 24–26 April 2017; Volume 2017, pp. 1–16.
29. Zhou, R.; Guo, F.; Azarpazhooh, M.R.; Hashemi, S.; Cheng, X.; Spence, J.D.; Ding, M.; Fenster, A. Deep Learning-Based Measurement of Total Plaque Area in B-Mode Ultrasound Images. IEEE J. Biomed. Heal. Inform. 2021, 25, 2194. [CrossRef] [PubMed]
30. Simantiris, G.; Tziritas, G. Cardiac MRI Segmentation with a Dilated CNN Incorporating Domain-Specific Constraints. IEEE J. Sel. Top. Signal Process. 2020, 4553, 1–9. [CrossRef]
31. Qin, J.; Huang, Y.; Wen, W. Multi-scale Feature Fusion Residual Network for Single Image Super-Resolution. Neurocomputing. 2020, 379, 334–342. [CrossRef]
32. Cetin, I.; Sanroma, G.; Petersen, S.E.; Napel, S.; Camara, O.; Ballester, M.G.; Lekadir, K. A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI. Lect. Notes Comput. Sci. (Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform.) 2018, 10663 LNCS, 82–90. [CrossRef]
33. Jefferies, J.L.; Towbin, J.A. Dilated Cardiomyopathy. Lancet. 2010, 375, 752–762. [CrossRef]
34. Maron, B.J.; Maron, M.S. Hypertrophic Cardiomyopathy. Lancet 2013, 381, 242–255. [CrossRef]