Sonstiges: |
- Nachgewiesen in: MEDLINE
- Sprachen: English
- Publication Type: Journal Article
- Language: English
- [Med Biol Eng Comput] 2024 Jan; Vol. 62 (1), pp. 61-70. <i>Date of Electronic Publication: </i>2023 Aug 24.
- MeSH Terms: Image Processing, Computer-Assisted* ; Neural Networks, Computer*
- Comments: Erratum in: Med Biol Eng Comput. 2023 Sep 19;:. (PMID: 37723383)
- References: Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35(5):1240–1251. (PMID: 10.1109/TMI.2016.253846526960222) ; Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp 234–241. Springer. ; Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J (2018) UNet++: a nested U-Net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, pp 3–11. Springer. ; Li D, Dharmawan DA, Ng BP, Rahardja S (2019) Residual U-Net for retinal vessel segmentation. In: 2019 IEEE International Conference on Image Processing (ICIP), pp 1425–1429. IEEE. ; Jha D, Smedsrud PH, Johansen D, de Lange T, Johansen HD, Halvorsen P, Riegler MA (2021) A comprehensive study on colorectal polyp segmentation with ResUNet++, conditional random field and test-time augmentation. IEEE journal of biomedical and health informatics 25(6):2029–2040. (PMID: 10.1109/JBHI.2021.304930433400658) ; Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3431–3440. ; Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) 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. (PMID: 10.1109/TPAMI.2017.269918428463186) ; Xia K, Yin H, Qian P, Jiang Y, Wang S (2019) Liver semantic segmentation algorithm based on improved deep adversarial networks in combination of weighted loss function on abdominal CT images. IEEE Access 7:96349–96358. (PMID: 10.1109/ACCESS.2019.2929270) ; Tang Y, Yang D, Li W, Roth HR, Landman B, Xu D, Nath V, Hatamizadeh A (2022) Self-supervised pre-training of swin transformers for 3D medical image analysis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 20730–20740. ; Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306. ; Lin A, Chen B, Xu J, Zhang Z, Lu G, Zhang D (2022) DS-TransUNet: dual swin transformer U-Net for medical image segmentation. IEEE Trans Instrum Meas 71:1–15. ; Liu L, Fan X, Zhang X, Hu Q (2022) Lightweight dual-domain network for real-time medical image segmentation. In: 2022 IEEE International Conference on Image Processing (ICIP), pp 396–400. IEEE. ; Shuvo MB, Ahommed R, Reza S, Hashem M (2021) CNL-UNet: a novel lightweight deep learning architecture for multimodal biomedical image segmentation with false output suppression. Biomedical Signal Processing and Control 70. ; Jahan MH, Imran AAZ (2022) LightSeg: efficient yet effective medical image segmentation. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp 1–4. IEEE. ; Wang P, Liu S, Peng J (2022) AST-Net: lightweight hybrid transformer for multimodal brain tumor segmentation. In: 2022 26th International Conference on Pattern Recognition (ICPR), pp 4623–4629. IEEE. ; Tolstikhin IO, Houlsby N, Kolesnikov A, Beyer L, Zhai X, Unterthiner T, Yung J, Steiner A, Keysers D, Uszkoreit J et al (2021) MLP-Mixer: an all-MLP architecture for vision. Advances in neural information processing systems 34:24261–24272. ; Valanarasu JMJ, Patel VM (2022) UNeXt: MLP-based rapid medical image segmentation network. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part V, pp 23–33. Springer. ; Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. ; Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A ConvNet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 11976–11986. ; Ding X, Zhang X, Han J, Ding G (2022) Scaling up your kernels to 31x31: revisiting large kernel design in CNNs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 11963–11975. ; Guo M-H, Lu C-Z, Liu Z-N, Cheng M-M, Hu S-M (2022) Visual attention network. arXiv preprint arXiv:2202.09741. ; Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp 448–456. pmlr. ; Wu Y, He K (2018) Group normalization. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 3–19. ; Huang L, Zhou Y, Wang T, Luo J, Liu X (2022) Delving into the estimation shift of batch normalization in a network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 763–772. ; Ma N, Zhang X, Zheng H-T, Sun J (2018) ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 116–131. ; Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 10012–10022. ; Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 11534–11542. ; Hu J, Shen L, Sun G (2018) Squeeze-and-Excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7132–7141. ; Lian D, Yu Z, Sun X, Gao S (2021) AS-MLP: an axial shifted MLP architecture for vision. arXiv preprint arXiv:2107.08391. ; Codella NC, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, Kalloo A, Liopyris K, Mishra N, Kittler H et al (2018) Skin lesion analysis toward melanoma detection: a challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), hosted by the International Skin Imaging Collaboration (ISIC). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp 168–172. IEEE. ; Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A (2020) Dataset of breast ultrasound images. Data in brief 28:104863. (PMID: 10.1016/j.dib.2019.10486331867417) ; Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 801–818. ; Chaurasia A, Culurciello E (2017) LinkNet: Exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp 1–4. IEEE.
- Grant Information: CHDKJ22-02-88 Ahai Power Generation Branch of Yunnan Huadian Jinsha River Middle Water Power Development Co., Ltd and China Huadian Corporation
- Contributed Indexing: Keywords: Deep learning; LMU-Net; Lightweight networks; Medical image segmentation
- Entry Date(s): Date Created: 20230824 Date Completed: 20240103 Latest Revision: 20240103
- Update Code: 20240103
|