Abstract
Artificial intelligence-based Medical Image Analysis (AI-MIA) has achieved significant advantages in accuracy and efficiency in various biomedical applications. However, most existing deep learning (DL) models have a fixed network structure and aim at single MIA tasks, making it difficult to meet the diverse and complex MIA requirements. To improve the universality of the AI-MIA DL models, we propose a configurable DL framework, which consists of a MIA task element library and a DL model component library. We establish a MIA task element library to accurately define various potential MIA tasks for numerous diseases. In addition, we collect DL computation modules that can be used in different steps of MIA and built a DL model component library. Given a specific MIA demand of different diseases, the framework can build a personalized DL model by defining MIA tasks and configuring DL model components. Moreover, by comparing with the existing machine learning (ML)/DL models, the structure of the DL model for each configuration can be further adjusted to improve its performance. To demonstrate the effectiveness of the proposed framework, two personalized DL models are designed for upper abdominal organ detection and colon cancer cell classification, achieving high accuracy and high performance. Experimental results on actual medical image datasets show that the configurable DL framework can define specific MIA requirements for different diseases and flexibly configure DL model components to build suitable personalized DL models. Compared with the state-of-the-art methods, the significant advantage of our framework is that it can generate personalized DL models with a flexible structure and can continuously fine-tune the model structure to improve model performance.
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References
Akmal TAMTZ, Than JCM, Abdullah H et al (2019) Chest X-ray image classification on common thorax diseases using GLCM and alexnet deep features. Int J Integr Eng 11(4)
Cancer COSMI (2020) Cosmic. Website. https://cancer.sanger.ac.uk/cosmic
Chakradhar S, Sankaradas M, Jakkula V, Cadambi S (2010) A dynamically configurable coprocessor for convolutional neural networks. In: Annual International Symposium on Computer Architecture (ISCA), pp. 247–257. IEEE
Chen Z, Xu Y, Chen E, Yang T (2018) Sadagrad: Strongly adaptive stochastic gradient methods. In: International Conference on Machine Learning (ICML), pp. 913–921
Ciresan D, Giusti A, Gambardella LM, Schmidhuber J (2012) Deep neural networks segment neuronal membranes in electron microscopy images. In: Advances in Neural Information Processing Systems, pp. 2843–2851
Duan K, Bai S, Xie L, Qi H, Huang Q, Tian Q (2019) Centernet: Keypoint triplets for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6569–6578
Gao F, Wu T, Chu X, Yoon H, Xu Y, Patel B (2019) Deep residual inception encoder-decoder network for medical imaging synthesis. IEEE J Biomed Health Inform 24(1):39–49
Goodfellow I, Pouget-Abadie J, Mirza M, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680
Guo X, Liu X, Zhu E, Yin J (2017) Deep clustering with convolutional autoencoders. In: International Conference on Neural Information Processing, pp. 373–382. Springer
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778. IEEE
He S, Wu Q, Saunders J (2006) A group search optimizer for neural network training. In: International Conference on Computational Science and its Applications, pp. 934–943. Springer
Huang B, Tian J, Zhang H et al (2021) Deep Semantic segmentation feature-based radiomics for the classification tasks in medical image analysis. IEEE J Biomed Health Info 25(7):2655–2664
Kawahara J, Brown CJ, Miller SP, Booth BG, Chau V, Grunau RE (2017) Brainnetcnn: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146:1038–1049
Khalifa IA, Zeebaree SR, Ataş M, Khalifa FM (2019) Image steganalysis in frequency domain using co-occurrence matrix and bpnn. Sci J Univ Zakho 7(1):27–32
Laina I, Rupprecht C, Belagiannis V, Tombari F, Navab N (2016) Deeper depth prediction with fully convolutional residual networks. In: International Conference on 3D Vision (3DV), pp. 239–248. IEEE
Li X, Dou Q, Chen H, Fu CW, Qi X, Belavỳ DL (2018) 3d multi-scale fcn with random modality voxel dropout learning for intervertebral disc localization and segmentation from multi-modality mr images. Med Image Anal 45:41–54
Liao F, Liang M, Li Z, Hu X, Song S (2019) Evaluate the malignancy of pulmonary nodules using the 3-d deep leaky noisy-or network. IEEE Trans Neural Netw Learn Syst 30(11):3484–3495
Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp. 2980–2988
Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440. IEEE
Ma L, Lu G, Wang D, Qin X, Chen ZG, Fei B (2019) Adaptive deep learning for head and neck cancer detection using hyperspectral imaging. Vis Comput Ind Biomed Art 2(1):1–12
O’Donoghue J, Roantree M, Van Boxtel M (2015) A configurable deep network for high-dimensional clinical trial data. In: International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE
(2021) PyTorch: Pytorch. Website. https://www.pytorch.org
Qian J, Yang J, Xu Y, Xie J, Lai Z, Zhang B (2020) Image decomposition based matrix regression with applications to robust face recognition. Pattern Recognit 102:107204
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788. IEEE
Sharma S, Sharma S, Athaiya A (2017) Activation functions in neural networks. Towards Data Sci 6(12):310–316
Sinha A, Dolz J (2020) Multi-scale self-guided attention for medical image segmentation. IEEE J Biomed Health Info 25(1):121–130
Sun J, Wang J, Gui G (2020) Adaptive deep learning aided digital predistorter considering dynamic envelope. IEEE Trans Veh Technol 69(4):4487–4491
Sun L, Wang J, Huang Y et al (2020) An adversarial learning approach to medical image synthesis for lesion detection. IEEE J Biomed Health Info 24(8):2303–2314
Tajbakhsh N, Jeyaseelan L, Li Q, Chiang JN, Wu Z, Ding X (2020) Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med Image Anal 63:101693
Tang S, Shen C, Wang D, Li S, Huang W, Zhu Z (2018) Adaptive deep feature learning network with nesterov momentum and its application to rotating machinery fault diagnosis. Neurocomputing 305:1–14
Wang Z, Zineddin B, Liang J, Zeng N, Li Y, Du M, Cao J, Liu X (2013) A novel neural network approach to cdna microarray image segmentation. Comput Methods Progr Biomed 111(1):189–198
Xiong Z, Fedorov VV, Fu X, Cheng E, Macleod R, Zhao J (2018) Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network. IEEE Trans Med Imaging 38(2):515–524
Yuan Y, Fang J, Lu X, Feng Y (2019) Spatial structure preserving feature pyramid network for semantic image segmentation. ACM Trans Multimed Comput Commun Appl 15(3):1–19
Zakaria Z, Suandi SA, Mohamad-Saleh J (2018) Hierarchical skin-adaboost-neural network (h-skann) for multi-face detection. Appl Soft Comput 68:172–190
Zeng N, Li H, Wang Z, Liu W, Liu S, Alsaadi FE, Liu X (2021) Deep-reinforcement-learning-based images segmentation for quantitative analysis of gold immunochromatographic strip. Neurocomputing 425:173–180
Zhang J, Liu M, Shen D (2017) Detecting anatomical landmarks from limited medical imaging data using two-stage task-oriented deep neural networks. IEEE Trans Image Process 26(10):4753–4764
Zhao H, Gallo O, Frosio I, Kautz J (2016) Loss functions for image restoration with neural networks. IEEE Trans Comput Imaging 3(1):47–57
Zhao Z, Wang Z, Zou L, Liu H (2018) Finite-horizon \(h_{\infty }\) state estimation for artificial neural networks with component-based distributed delays and stochastic protocol. Neurocomputing 321:169–177
Zheng G, Liu X, Han G (2018) A review of computer-aided detection and diagnosis system for medical imaging. J Softw 29(5):1471–1514
Acknowledgements
This work was partially funded by the National Natural Science Foundation of China under Grant 62002110, and the International Postdoctoral Exchange Fellowship Program under Grant 20180024.
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Chen, J., Yang, N., Zhou, M. et al. A configurable deep learning framework for medical image analysis. Neural Comput & Applic 34, 7375–7392 (2022). https://doi.org/10.1007/s00521-021-06873-z
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DOI: https://doi.org/10.1007/s00521-021-06873-z