Brief Overview of Neural Networks for Medical Applications

[1] K. S. CHOI and L. SUNWOO. “Artificial Intelligence in Neuroimaging: Clinical Applications”. In: Investigative Magnetic Resonance Imaging 26.1 (2022), pp. 1–9. Search in Google Scholar

[2] D. S. LARA-MARTINEZ et al. “Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography”. In: American Heart Journal Plus: Cardiology Research and Practice (2022), p. 100129.10.1016/j.ahjo.2022.100129920299635721662 Search in Google Scholar

[3] T. B. SHAMS et al. “EEG-based Biometric Authentication Using Machine Learning: A Comprehensive Survey”. In: (). Search in Google Scholar

[4] G. CHOY et al. “Current applications and future impact of machine learning in radiology”. In: Radiology 288.2 (2018), pp. 318–328. Search in Google Scholar

[5] M. L. GIGER. “Machine learning in medical imaging”. In: Journal of the American College of Radiology 15.3 (2018), pp. 512–520.10.1016/j.jacr.2017.12.02829398494 Search in Google Scholar

[6] A. GHORBANI, A. ABID, and J. ZOU. “Interpretation of neural networks is fragile”. In: Proceedings of the AAAI conference on artificial intelligence. Vol. 33. 01. 2019, pp. 3681–3688.10.1609/aaai.v33i01.33013681 Search in Google Scholar

[7] I. GOODFELLOW, Y. BENGIO, and A. COURVILLE. Deep Learning. http://www.deeplearningbook.org. MIT Press, 2016. ISBN: 9780262035613. Search in Google Scholar

[8] G. CYBENKO. “Approximation by superpositions of a sigmoidal function”. In: Mathematics of Control, Signals, and Systems (MCSS) 2.4 (Dec. 1989), pp. 303–314. ISSN: 0932-4194.10.1007/BF02551274 Search in Google Scholar

[9] K. HORNIK, M. STINCHCOMBE, and H. WHITE. “Multilayer feedforward networks are universal approximators”. In: Neural Networks 2.5 (1989), pp. 359–366. ISSN: 0893-6080. Search in Google Scholar

[10] M. A. KRAMER. “Nonlinear principal component analysis using autoassociative neural networks”. In: AIChE Journal 7 (Feb. 1991), pp. 233–243.10.1002/aic.690370209 Search in Google Scholar

[11] S. CHAURASIA, S. GOYAL, and M. RAJPUT. “Outlier Detection Using Autoencoder Ensembles: A Robust Unsupervised Approach”. In: 2020 International Conference on Contemporary Computing and Applications (IC3A) (2020), pp. 76–80. Search in Google Scholar

[12] D. H. HUBEL and T. N. WIESEL. “Receptive fields and functional architecture of monkey striate cortex”. In: The Journal of physiology 195.1 (1968), pp. 215–243. Search in Google Scholar

[13] C. C. AGGARWAL et al. “Neural networks and deep learning”. In: Springer 10 (2018), pp. 978–3. Search in Google Scholar

[14] J. GU et al. “Recent advances in convolutional neural networks”. In: Pattern Recognition 77 (2018), pp. 354–377.10.1016/j.patcog.2017.10.013 Search in Google Scholar

[15] A. HYVÄRINEN and U. KÖSTER. “Complex cell pooling and the statistics of natural images”. In: Network: Computation in Neural Systems 18.2 (2007), pp. 81–100. Search in Google Scholar

[16] M. A. WANI et al. Advances in deep learning. Springer, 2020.10.1007/978-981-13-6794-6 Search in Google Scholar

[17] Y. LECUN et al. “Handwritten digit recognition with a back-propagation network”. In: Advances in neural information processing systems. 1990, pp. 396–404. Search in Google Scholar

[18] Y. LECUN et al. “Gradient-based learning applied to document recognition”. In: Proceedings of the IEEE 86.11 (1998), pp. 2278–2324.10.1109/5.726791 Search in Google Scholar

[19] A. KRIZHEVSKY, I. SUTSKEVER, and G. E. HINTON. “Imagenet classification with deep convolutional neural networks”. In: Advances in neural information processing systems 25 (2012), pp. 1097–1105. Search in Google Scholar

[20] K. HE et al. “Deep residual learning for image recognition”. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, pp. 770–778.10.1109/CVPR.2016.90 Search in Google Scholar

[21] K. SIMONYAN and A. ZISSERMAN. “Very deep convolutional networks for large-scale image recognition”. In: arXiv preprint arXiv:1409.1556 (2014). Search in Google Scholar

[22] C. SZEGEDY et al. “Going deeper with convolutions”. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015, pp. 1–9.10.1109/CVPR.2015.7298594 Search in Google Scholar

[23] W. ZHIQIANG and L. JUN. “A review of object detection based on convolutional neural network”. In: 2017 36th Chinese Control Conference (CCC). IEEE. 2017, pp. 11104–11109.10.23919/ChiCC.2017.8029130 Search in Google Scholar

[24] A. DHILLON and G. K. VERMA. “Convolutional neural network: a review of models, methodologies and applications to object detection”. In: Progress in Artificial Intelligence 9.2 (2020), pp. 85–112. Search in Google Scholar

[25] T. GUO et al. “Simple convolutional neural network on image classification”. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA). IEEE. 2017, pp. 721–724.10.1109/ICBDA.2017.8078730 Search in Google Scholar

[26] F. SULTANA, A. SUFIAN, and P. DUTTA. “Advancements in Image Classification using Convolutional Neural Network”. In: 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). 2018, pp. 122–129.10.1109/ICRCICN.2018.8718718 Search in Google Scholar

[27] L. VAVREK et al. “Deep convolutional neural network for detection of pathological speech”. In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE. 2021, pp. 000245–000250.10.1109/SAMI50585.2021.9378656 Search in Google Scholar

[28] M. HIREŠ et al. “Convolutional neural network ensemble for Parkinson’s disease detection from voice recordings”. In: Computers in biology and medicine (2021), p. 105021.10.1016/j.compbiomed.2021.10502134799077 Search in Google Scholar

[29] G. H. NAIR, V. REKHA, and M. SOUMYA Krishnan. “Handwriting Analysis Using Deep Learning Approach for the Detection of Personality Traits”. In: Ubiquitous Intelligent Systems. Springer, 2022, pp. 531–539.10.1007/978-981-16-3675-2_40 Search in Google Scholar

[30] M. GAZDA, M. HIREŠ, and P. DROTÁR. “Multiple-fine-tuned convolutional neural networks for Parkinson’s disease diagnosis from offline handwriting”. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems 52.1 (2021), pp. 78–89. Search in Google Scholar

[31] L. ANTONI et al. “A Two-Phase Multilabel ECG Classification Using One-Dimensional Convolutional Neural Network and Modified Labels”. In: Computing in Cardiology 2021 48 (2021), pp. 1–4.10.23919/CinC53138.2021.9662878 Search in Google Scholar

[32] M. GAZDA et al. “Self-Supervised Deep Convolutional Neural Network for Chest X-Ray Classification”. In: IEEE Access 9 (2021), pp. 151972–151982.10.1109/ACCESS.2021.3125324 Search in Google Scholar

[33] Q. LI et al. “Medical image classification with convolutional neural network”. In: 2014 13th International Conference on Control Automation Robotics Vision (ICARCV). 2014, pp. 844–848.10.1109/ICARCV.2014.7064414 Search in Google Scholar

[34] M. GAZDA et al. “Mixup Augmentation for Kidney and Kidney Tumor Segmentation”. In: Kidney and Kidney Tumor Segmentation. Ed. by N. HELLER et al. Cham: Springer International Publishing, 2022, pp. 90–97. ISBN: 978-3-030-98385-7. Search in Google Scholar

[35] S. HOCHREITER and J. SCHMIDHUBER. “Long Short-Term Memory”. In: Neural Computation 9.8 (1997), pp. 1735–1780. Search in Google Scholar

[36] Y. BENGIO, P. SIMARD, and P. FRASCONI. “Learning long-term dependencies with gradient descent is difficult”. In: IEEE Transactions on Neural Networks 5.2 (1994), pp. 157–166. Search in Google Scholar

[37] R. PASCANU, T. MIKOLOV, and Y. BENGIO. “On the Difficulty of Training Recurrent Neural Networks”. In: Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28. ICML’13. Atlanta, GA, USA: JMLR.org, 2013, III–1310–III–1318. Search in Google Scholar

[38] A. GRAVES. Supervised Sequence Labelling with Recurrent Neural Networks. Vol. 385. Studies in Computational Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. ISBN: 978-3-642-24797-2. Search in Google Scholar

[39] I. SUTSKEVER, O. VINYALS, and Q. V. LE. Sequence to Sequence Learning with Neural Networks. 2014. Search in Google Scholar

[40] A. GRAVES, A.-r. MOHAMED, and G. HINTON. “Speech recognition with deep recurrent neural networks”. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. 2013, pp. 6645–6649.10.1109/ICASSP.2013.6638947 Search in Google Scholar

[41] A. GRAVES. Generating Sequences With Recurrent Neural Networks. 2014. Search in Google Scholar

[42] A. GUPTA et al. “Generative Recurrent Networks for De Novo Drug Design”. In: Molecular Informatics 37.1-2 (2018), p. 1700111.10.1002/minf.201700111583694329095571 Search in Google Scholar

[43] S. KUMAR and D. SUBHA. “Prediction of Depression from EEG Signal Using Long Short Term Memory(LSTM)”. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). 2019, pp. 1248–1253.10.1109/ICOEI.2019.8862560 Search in Google Scholar

[44] J. PENG and Y. WANG. “Medical image segmentation with limited supervision: A review of deep network models”. In: IEEE Access 9 (2021), pp. 36827–36851. Search in Google Scholar

[45] O. RONNEBERGER, P. Fischer, and T. Brox. “UNet: Convolutional Networks for Biomedical Image Segmentation”. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Ed. by N. Navab et al. Cham: Springer International Publishing, 2015, pp. 234–241. ISBN: 978-3-319-24574-4. Search in Google Scholar

[46] M. DROZDZAL et al. “The Importance of Skip Connections in Biomedical Image Segmentation”. In: Deep Learning and Data Labeling for Medical Applications. Ed. by G. Carneiro et al. Cham: Springer International Publishing, 2016, pp. 179–187. ISBN: 978-3-319-46976-8. Search in Google Scholar

[47] G. HUANG et al. “Densely Connected Convolutional Networks”. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017).10.1109/CVPR.2017.243 Search in Google Scholar

[48] P. BILIC et al. “The liver tumor segmentation benchmark (lits)”. In: arXiv preprint arXiv:1901.04056 (2019). Search in Google Scholar

[49] X. GUO et al. “Retinal vessel segmentation combined with generative adversarial networks and dense U-net”. In: IEEE Access 8 (2020), pp. 194551–194560.10.1109/ACCESS.2020.3033273 Search in Google Scholar

[50] K. C. SIONTIS et al. “Artificial intelligence-enhanced electrocardiography in cardiovascular disease management”. In: Nature Reviews Cardiology (2021). ISSN: 1759-5010. Search in Google Scholar

[51] M. ELGENDI et al. “Revisiting QRS Detection Methodologies for Portable, Wearable, Battery-Operated, and Wireless ECG Systems”. In: PLOS ONE 9.1 (Jan. 2014), pp. 1–18.10.1371/journal.pone.0084018388365424409290 Search in Google Scholar

[52] P. WAGNER et al. “PTB-XL, a large publicly available electrocardiography dataset”. In: Scientific Data 7.154 (2020). ISSN: 2052-4463.10.1038/s41597-020-0495-6724807132451379 Search in Google Scholar

[53] G. B. MOODY and R. G. Mark. “The impact of the MIT-BIH Arrhythmia Database”. In: IEEE Engineering in Medicine and Biology Magazine 20.3 (2001), pp. 45–50. Search in Google Scholar

[54] S. VIJAYARANGAN et al. “RPnet: A Deep Learning approach for robust R Peak detection in noisy ECG”. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC). 2020, pp. 345–348.10.1109/EMBC44109.2020.917608433017999 Search in Google Scholar

[55] A. Y. HANNUN et al. “Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network”. In: Nature Medicine 25 (2019), pp. 65–69. ISSN: 1546-170X.10.1038/s41591-018-0268-3678483930617320 Search in Google Scholar

[56] P. RAJPURKAR et al. “Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks”. In: arXiv preprint arXiv:1707.01836 (2017). Search in Google Scholar

[57] M. TURAKHIA et al. “Diagnostic utility of a novel leadless arrhythmia monitoring device”. English (US). In: American Journal of Cardiology 112.4 (Aug. 2013), pp. 520–524. ISSN: 0002-9149.10.1016/j.amjcard.2013.04.01723672988 Search in Google Scholar

[58] F. LIU et al. “An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection”. In: Journal of Medical Imaging and Health Informatics 8 (2018), pp. 1368–1373.10.1166/jmihi.2018.2442 Search in Google Scholar

[59] E. A. PEREZ ALDAY et al. “Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020”. In: Physiological Measurement 41.12 (2020).10.1088/1361-6579/abc960801578933176294 Search in Google Scholar

[60] M. A. REYNA et al. “Will Two Do? Varying Dimensions in Electrocardiography: the PhysioNet/Computing in Cardiology Challenge 2021”. In: Computing in Cardiology 2021 48 (2021), pp. 1–4.10.23919/CinC53138.2021.9662687 Search in Google Scholar

[61] F. N. IANDOLA et al. “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and¡ 0.5 MB model size”. In: arXiv preprint arXiv:1602.07360 (2016). Search in Google Scholar

[62] A. ESTEVA et al. “Dermatologist-level classification of skin cancer with deep neural networks”. In: nature 542.7639 (2017), pp. 115–118.10.1038/nature21056838223228117445 Search in Google Scholar

[63] F. J. D´AZ-PERNAS et al. “A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network”. In: Healthcare. Vol. 9. 2. Multidisciplinary Digital Publishing Institute. 2021, p. 153.10.3390/healthcare9020153791294033540873 Search in Google Scholar

[64] P. LAKHANI and B. SUNDARAM. “Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks”. In: Radiology 284.2 (2017), pp. 574–582. Search in Google Scholar

[65] Y. IKENOYAMA et al. “Detecting early gastric cancer: Comparison between the diagnostic ability of convolutional neural networks and endoscopists”. In: Digestive Endoscopy 33.1 (2021), pp. 141–150.10.1111/den.13688781818732282110 Search in Google Scholar

[66] L. WU et al. “A deep neural network improves endoscopic detection of early gastric cancer without blind spots”. In: Endoscopy 51.06 (2019), pp. 522–531.10.1055/a-0855-353230861533 Search in Google Scholar

[67] L. LI et al. “Attention based glaucoma detection: a large-scale database and CNN model”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019, pp. 10571–10580.10.1109/CVPR.2019.01082 Search in Google Scholar

[68] O. RONNEBERGER, P. FISCHER, and T. BROX. “U-net: Convolutional networks for biomedical image segmentation”. In: International Conference on Medical image computing and computer-assisted intervention. Springer. 2015, pp. 234–241.10.1007/978-3-319-24574-4_28 Search in Google Scholar

[69] F. ISENSEE et al. “nnu-net: Self-adapting framework for u-net-based medical image segmentation”. In: arXiv preprint arXiv:1809.10486 (2018).10.1007/978-3-658-25326-4_7 Search in Google Scholar

[70] N. HELLER et al. “The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge”. In: Medical image analysis 67 (2021), p. 101821.10.1016/j.media.2020.101821773420333049579 Search in Google Scholar

[71] F. ISENSEE et al. “Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge”. In: International MICCAI Brainlesion Workshop. Springer. 2017, pp. 287–297.10.1007/978-3-319-75238-9_25 Search in Google Scholar

[72] A. GRAVES, A.-R. MOHAMED, and G. HINTON. “Speech recognition with deep recurrent neural networks”. In: 2013 IEEE international conference on acoustics, speech and signal processing. Ieee. 2013, pp. 6645–6649.10.1109/ICASSP.2013.6638947 Search in Google Scholar

[73] Z. YUE et al. “Automatic CIN grades prediction of sequential cervigram image using LSTM with multi-state CNN features”. In: IEEE journal of biomedical and health informatics 24.3 (2019), pp. 844–854.10.1109/JBHI.2019.292268231199278 Search in Google Scholar

[74] Y. JIN et al. “SV-RCNet: workflow recognition from surgical videos using recurrent convolutional network”. In: IEEE transactions on medical imaging 37.5 (2017), pp. 1114–1126.10.1109/TMI.2017.278765729727275 Search in Google Scholar

[75] X. SHI et al. “Convolutional LSTM network: A machine learning approach for precipitation nowcasting”. In: Advances in neural information processing systems 28 (2015). Search in Google Scholar

[76] M. F. STOLLENGA et al. “Parallel multidimensional LSTM, with application to fast biomedical volumetric image segmentation”. In: Advances in neural information processing systems 28 (2015), pp. 2998–3006. Search in Google Scholar

[77] A. KONWER et al. “Predicting COVID-19 lung infiltrate progression on chest radiographs using spatio-temporal LSTM based encoder-decoder network”. In: Medical Imaging with Deep Learning. PMLR. 2021, pp. 384–398. Search in Google Scholar

[78] D. ZHANG et al. “A multi-level convolutional LSTM model for the segmentation of left ventricle myocardium in infarcted porcine cine MR images”. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). 2018, pp. 470–473.10.1109/ISBI.2018.8363618 Search in Google Scholar

[79] R. GAO et al. “Distanced LSTM: time-distanced gates in long short-term memory models for lung cancer detection”. In: International Workshop on Machine Learning in Medical Imaging. Springer. 2019, pp. 310–318.10.1007/978-3-030-32692-0_36 Search in Google Scholar

[80] J. CAI et al. “Improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function”. In: arXiv preprint arXiv:1707.04912 (2017). Search in Google Scholar

[81] R. SANTERAMO, S. WITHEY, and G. MONTANA. “Longitudinal detection of radiological abnormalities with time-modulated LSTM”. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, 2018, pp. 326–333.10.1007/978-3-030-00889-5_37 Search in Google Scholar

[82] N. BRAMAN, D. BEYMER, and E. DEHGHAN. “Disease detection in weakly annotated volumetric medical images using a convolutional LSTM network”. In: arXiv preprint arXiv:1812.01087 (2018). Search in Google Scholar

[83] D. JIANG et al. “Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models”. In: Journal of cheminformatics 13.1 (2021), pp. 1–23.10.1186/s13321-020-00479-8788818933597034 Search in Google Scholar

[84] M. M. BRONSTEIN et al. “Geometric deep learning: Grids, groups, graphs, geodesics, and gauges”. In: arXiv preprint arXiv:2104.13478 (2021). Search in Google Scholar