Dendritic Learning and Miss Region Detection-Based Deep Network for Multi-scale Medical Segmentation
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OpenAlex topics: AI in cancer detection · Advanced Neural Network Applications · Radiomics and Machine Learning in Medical Imaging
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References (37)
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1. Bhattacharya, S., Reddy Maddikunta, P. K., Pham, Q.-V., Gadekallu, T. R., Krishnan, S., Chowdhary, C. L., Alazab, M., & Jalil Piran, M. (2021). Deep learning and medical image processing for coronavirus (covid-19) pandemic: A survey. Sustainable Cities and Society, 65, 102589.10.1016/j.scs.2020.102589 ↗
2. Mukherjee, P., Zhou, M., Lee, E., Schicht, A., Balagurunathan, Y., Napel, S., Gillies, R., Wong, S., Thieme, A., Leung, A., & Gevaert, O. (2020). A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets. Nature Machine Intelligence, 2, 274–282.10.1038/s42256-020-0173-6 ↗
3. Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., & Terzopoulos, D. (2022). Image segmentation using deep learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(7), 3523–3542.
4. Zhou, T., Li, L., Bredell, G., Li, J., Unkelbach, J., & Konukoglu, E. (2023). Volumetric memory network for interactive medical image segmentation. Medical Image Analysis, 83, 102599.10.1016/j.media.2022.102599 ↗
5. Zhang, Y., Peng, C., Tong, R., Lin, L., Chen, Y.-W., Chen, Q., Hu, H., & Zhou, S. K. (2023). Multi-modal tumor segmentation with deformable aggregation and uncertain region inpainting. IEEE Transactions on Medical Imaging, 42(10), 3091–3103.10.1109/TMI.2023.3275592 ↗
6. Mirikharaji, Z., Abhishek, K., Bissoto, A., Barata, C., Avila, S., Valle, E., Celebi, M. E., & Hamarneh, G. (2023). A survey on deep learning for skin lesion segmentation. Medical Image Analysis, 88, 102863.10.1016/j.media.2023.102863 ↗
7. Tariq, M., Iqbal, S., Ayesha, H., Abbas, I., Ahmad, K. T., & Niazi, M. F. K. (2021). Medical image based breast cancer diagnosis: State of the art and future directions. Expert Systems With Applications, 167, 114095.10.1016/j.eswa.2020.114095 ↗
8. Huang, Y., Yang, X., Liu, L., Zhou, H., Chang, A., Zhou, X., Chen, R., Yu, J., Chen, J., Chen, C., Liu, S., Chi, H., Hu, X., Yue, K., Li, L., Grau, V., Fan, D.-P., Dong, F., & Ni, D. (2024). Segment anything model for medical images? Medical Image Analysis, 92, 103061.10.1016/j.media.2023.103061 ↗
9. Upadhyay, A. K., & Bhandari, A. K. (2023). Semi-supervised modified-unet for lung infection image segmentation. IEEE Transactions on Radiation and Plasma Medical Sciences, 7(6), 638–649.10.1109/TRPMS.2023.3272209 ↗
10. Zhang, J., Chen, D., Ma, D., Ying, C., Sun, X., Xu, X., & Cheng, Y. (2023). Cdcsegnet: Automatic covid-19 infection segmentation from ct images. IEEE Transactions on Instrumentation and Measurement, 72, 1–13.
11. Chen, X., Wang, X., Zhang, K., Fung, K.-M., Thai, T. C., Moore, K., Mannel, R. S., Liu, H., Zheng, B., & Qiu, Y. (2022). Recent advances and clinical applications of deep learning in medical image analysis. Medical Image Analysis, 79, 102444.10.1016/j.media.2022.102444 ↗
12. Ronneberger, O., Fischer, P., & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Munich, Germany, 2015, pp. 234–241.10.1007/978-3-319-24574-4_28 ↗
13. Jha, D., Smedsrud, P. H., Riegler, M. A., Johansen, D., Lange, T. D., Halvorsen, P., & Johansen, H. Resunet++: An advanced architecture for medical image segmentation. In: 2019 IEEE International Symposium on Multimedia (ISM), San Diego, USA, 2019, pp. 225–2255.10.1109/ISM46123.2019.00049 ↗
14. Zhang, Y., Chen, J., Ma, X., Wang, G., Bhatti, U. A., & Huang, M. (2024). Interactive medical image annotation using improved attention u-net with compound geodesic distance. Expert Systems With Applications, 237, 121282.10.1016/j.eswa.2023.121282 ↗
15. Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495.10.1109/TPAMI.2016.2644615 ↗
16. Xing, W., Zhu, Z., Hou, D., Yue, Y., Dai, F., Li, Y., Tong, L., Song, Y., & Ta, D. (2022). Cm-segnet: A deep learning-based automatic segmentation approach for medical images by combining convolution and multilayer perceptron. Computers in Biology and Medicine, 147, 105797.10.1016/j.compbiomed.2022.105797 ↗
17. Han, Q., Wang, H., Hou, M., Weng, T., Pei, Y., Li, Z., Chen, G., Tian, Y., & Qiu, Z. (2023). Hwa-segnet: Multi-channel skin lesion image segmentation network with hierarchical analysis and weight adjustment. Computers in Biology and Medicine, 152, 106343.10.1016/j.compbiomed.2022.106343 ↗
18. Xiong, X., Zhu, T., Zhu, Y., Cao, M., Xiao, J., Li, L., Wang, F., Fan, C., & Pei, H. (2022). Molecular convolutional neural networks with dna regulatory circuits. Nature Machine Intelligence, 4, 625–635.10.1038/s42256-022-00502-7 ↗
19. Gao, S., Zhou, M., Wang, Y., Cheng, J., Yachi, H., & Wang, J. (2019). Dendritic neural model with effective learning algorithms for classification, approximation, and prediction. IEEE Transactions on Neural Networks and Learning Systems, 30(2), 601–604.10.1109/TNNLS.2018.2846646 ↗
20. Yu, Y., Lei, Z., Wang, Y., Zhang, T., Peng, C., & Gao, S. (2022). Improving dendritic neuron model with dynamic scale-free network-based differential evolution. IEEE/CAA Journal of Automatica Sinica, 9(1), 99–110.10.1109/JAS.2021.1004284 ↗
21. Tang, Y., Song, Z., Zhu, Y., Hou, M., Tang, C., & Ji, J. (2022). Adopting a dendritic neural model for predicting stock price index movement. Expert Systems With Applications, 205, 117637.10.1016/j.eswa.2022.117637 ↗
22. Zhang, Z., Lei, Z., Omura, M., Hasegawa, H., & Gao, S. (2024). Dendritic learning-incorporated vision transformer for image recognition. IEEE/CAA Journal of Automatica Sinica, 11(2), 539–541.10.1109/JAS.2023.123978 ↗
23. Gao, S., Zhou, M., Wang, Z., Sugiyama, D., Cheng, J., Wang, J., & Todo, Y. (2023). Fully complex-valued dendritic neuron model. IEEE Transactions on Neural Networks and Learning Systems, 34(4), 2105–2118.10.1109/TNNLS.2021.3105901 ↗
24. Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H. R., & Xu, D. Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, USA, 2022, pp. 574–584.10.1109/WACV51458.2022.00181 ↗
25. Milletari, F., Navab, N., & Ahmadi, S.-A. V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), Stanford, USA, 2016, pp. 565–571.10.1109/3DV.2016.79 ↗
26. Zhang, S., & Zhang, C. (2023). Modified u-net for plant diseased leaf image segmentation. Computers and Electronics in Agriculture, 204, 107511.10.1016/j.compag.2022.107511 ↗
27. Beeche, C., Singh, J. P., Leader, J. K., Gezer, N. S., Oruwari, A. P., Dansingani, K. K., Chhablani, J., & Pu, J. (2022). Super u-net: A modularized generalizable architecture. Pattern Recognition, 128, 108669.10.1016/j.patcog.2022.108669 ↗
28. He, K., Zhang, X., Ren, S., & Sun, J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016, pp. 770–778.10.1109/CVPR.2016.90 ↗
29. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017, pp. 4700–4708.10.1109/CVPR.2017.243 ↗
30. Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848.10.1109/TPAMI.2017.2699184 ↗
31. Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 2018, pp. 801–818.10.1007/978-3-030-01234-2_49 ↗
32. Abraham, N., & Khan, N. M. A novel focal tversky loss function with improved attention u-net for lesion segmentation. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, 2019, pp. 683–687.10.1109/ISBI.2019.8759329 ↗
33. Jin, S., Yu, S., Jun, P., Wang, H., & Zhao, Y. (2023). A novel medical image segmentation approach by using multi-branch segmentation network based on local and global information synchronous learning. Scientific Reports, 13(1), 6762.10.1038/s41598-023-33357-y ↗
34. Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A. L., & Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv:2102.0430610.1109/IGARSS46834.2022.9883628 ↗
35. Yap, M. H., Pons, G., Marti, J., Ganau, S., Sentis, M., Zwiggelaar, R., Davison, A. K., & Marti, R. (2018). Automated breast ultrasound lesions detection using convolutional neural networks. IEEE Journal of Biomedical and Health Informatics, 22(4), 1218–1226.10.1109/JBHI.2017.2731873 ↗
36. Zhuang, Z., Li, N., Joseph Raj, A. N., Mahesh, V. G., & Qiu, S. (2019). An RDAU-NET model for lesion segmentation in breast ultrasound images. PLoS ONE, 14(8), 0221535.10.1371/journal.pone.0221535 ↗
37. Gao, S., Yu, Y., Wang, Y., Wang, J., Cheng, J., & Zhou, M. (2021). Chaotic local search-based differential evolution algorithms for optimization. IEEE Transactions on Systems, Man and Cybernetics: Systems, 51(6), 3954–3967.10.1109/TSMC.2019.2956121 ↗