ColonNeXt: Fully Convolutional Attention for Polyp Segmentation

Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., Vilariño, F.: Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Computerized Medical Imaging and Graphics. 43, 99–111 (2015) https://doi.org/10.1016/j.compmedimag.2015.02.007

Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. IEEE Transactions on Medical Imaging. 35(2), 630–644 (2016) https://doi.org/10.1109/TMI.2015.2487997

Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., Lange, T., Johansen, D., Johansen, H.D.: Kvasir-SEG: A Segmented Polyp Dataset (2019)

Silva, J., Histace, A., Romain, O., Dray, X., Bertrand, Granado: Towards embedded detection of polyps in wce images for early diagnosis of colorectal cancer. (2016)

Vázquez, D., Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., López, A.M., Romero, A., Drozdzal, M., Courville, A.: A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images (2016)

Fan, D.-P., Ji, G.-P., Zhou, T., Chen, G., Fu, H., Shen, J., Shao, L.: Pranet: Parallel reverse attention network for polyp segmentation. ArXiv. abs/2006.11392 (2020)

Lou, A., Guan, S., Loew, M.H.: Caranet: context axial reverse attention network for segmentation of small medical objects. In: Medical Imaging (2021)

Duc, N.T., Oanh, N.T., Thuy, N.T., Triet, T.M., Sang, D.V.: ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentation (2022)

Dumitru, R.-G., Peteleaza, D., Craciun, C.: Using duck-net for polyp image segmentation. Scientific Reports. 13(1) (2023) https://doi.org/10.1038/s41598-023-36940-5

Zhang, R., Li, G., Li, Z., Cui, S., Qian, D., Yu, Y.: Adaptive context selection for polyp segmentation. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI 23, pp. 253–262 (2020). Springer

Zhu, J., Ge, M., Chang, Z., Dong, W.: Crcnet: Global-local context and multi-modality cross attention for polyp segmentation. Biomedical Signal Processing and Control. 83, 104593 (2023) https://doi.org/10.1016/j.bspc.2023.104593

Nguyen, D.C., Nguyen, H.L.: Polypooling: An accurate polyp segmentation from colonoscopy images. Biomedical Signal Processing and Control. 92, 105979 (2024) https://doi.org/10.1016/j.bspc.2024.105979

Guo, M.-H., Lu, C.-Z., Hou, Q., Liu, Z., Cheng, M.-M., Hu, S.-M.: Segnext: Rethinking convolutional attention design for semantic segmentation. Advances in Neural Information Processing Systems. 35, 1140–1156 (2022)

Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: Convolutional Block Attention Module (2018)

Hao, K., Lin, S., Qiao, J., Tu, Y.: A generalized pooling for brain tumor segmentation. IEEE Access. 9, 159283–159290 (2021) https://doi.org/10.1109/ACCESS.2021.3130035

Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. CoRR. abs/1807.10165 (2018) arXiv:1807.10165

Jha, D., Smedsrud, P.H., Riegler, M.A., Johansen, D., De Lange, T., Halvorsen, P., Johansen, H.D.: Resunet++: An advanced architecture for medical image segmentation. In: 2019 IEEE International Symposium on Multimedia (ISM), pp. 225–2255 (2019). IEEE

Huang, D., Han, K., Xi, Y., Che, W.: Multi-scale fusion attention network for polyp segmentation. In: ICONIP 2021, pp. 160–167 (2021). Springer

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. (2020)

Dong, B., Wang, W., Fan, D.-P., Li, J., Fu, H., Shao, L.: Polyp-pvt: Polyp segmentation with pyramid vision transformers. arXiv preprint arXiv:2108.06932. (2021)

Jha, D., Tomar, N.K., Sharma, V., Bagci, U.: Transnetr: Transformer-based residual network for polyp segmentation with multi-center out-of-distribution testing. arXiv preprint arXiv:2303.07428. (2023)

Wang, J., Huang, Q., Tang, F., Meng, J., Su, J., Song, S.: Stepwise feature fusion: Local guides global. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 110–120 (2022). Springer

Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: 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 (2020)

Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in neural information processing systems. 34, 12077–12090 (2021)

Google Scholar 

Lou, A., Loew, M.: Cfpnet: Channel-wise feature pyramid for real-time semantic segmentation. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 1894–1898 (2021). IEEE

Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597–1604 (2009). https://doi.org/10.1109/CVPR.2009.5206596

Fan, D.-P., Cheng, M.-M., Liu, Y., Li, T., Borji, A.: Structure-measure: A new way to evaluate foreground maps. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4548–4557 (2017)

Fan, D.-P., Gong, C., Cao, Y., Ren, B., Cheng, M.-M., Borji, A.: Enhanced-alignment measure for binary foreground map evaluation. arXiv preprint arXiv:1805.10421. (2018)

Woolson, R.F.: Wilcoxon signed-rank test. Encyclopedia of Biostatistics. 8 (2005)

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K.P., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40, 834–848 (2016)

Article  Google Scholar 

Ronneberger, O., Fischer, P., Brox, T.: 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 (2015). Springer

Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging. 39(6), 1856–1867 (2019)

Article  PubMed  PubMed Central  Google Scholar 

Zhao, X., Zhang, L., Lu, H.: Automatic polyp segmentation via multi-scale subtraction network. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 120–130 (2021). Springer

Zhou, T., Zhou, Y., He, K., Gong, C., Yang, J., Fu, H., Shen, D.: Cross-level feature aggregation network for polyp segmentation. Pattern Recognition. 140, 109555 (2023) https://doi.org/10.1016/j.patcog.2023.109555

Jha, D., Smedsrud, P.H., Riegler, M.A., Johansen, D., Lange, T., Halvorsen, P., Johansen, H.D.: ResUNet++: An Advanced Architecture for Medical Image Segmentation (2019)

Jha, D., Riegler, M.A., Johansen, D., Halvorsen, P., Johansen, H.D.: DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation (2020)

Jha, D., Ali, S., Tomar, N.K., Johansen, H.D., Johansen, D., Rittscher, J., Riegler, M.A., Halvorsen, P.: Real-time polyp detection, localization and segmentation in colonoscopy using deep learning. IEEE Access. 9, 40496–40510 (2021) https://doi.org/10.1109/access.2021.3063716

Article  PubMed  Google Scholar 

Huang, C.-H., Wu, H.-Y., Lin, Y.-L.S.: Hardnet-mseg: A simple encoder-decoder polyp segmentation neural network that achieves over 0.9 mean dice and 86 fps. ArXiv. abs/2101.07172 (2021)

Tomar, N.K., Jha, D., Ali, S., Johansen, H.D., Johansen, D., Riegler, M.A., Halvorsen, P.: DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation (2020)

Comments (0)

No login
gif