Abstract
Medical image segmentation is a pivotal research domain that has garnered widespread attention in contemporary medical diagnostics. In pursuit of enhancing network efficacy, researchers have taken great efforts to develop various well-designed decoders. Unfortunately, due to the limited medical training data, the issues of underfitting and overfitting frequently arise. To this end, we undertake plentiful experiments to decouple the encoder and decoder components, and obtain a critical finding that excessively complex decoders impede the encoder’s potentiality of feature extraction. Inspired by some remarkable image generation work, we devise a straightforward segmentation network, which incorporates a pre-trained encoder backbone network and a pixel classification head. Our network not only ensures adequate feature decoding ability but also maximizes feature representation capability of the backbone. Experimental results on four datasets of three tasks show the outstanding performance against the state-of-the-art methods. The source code will be publicly available at https://github.com/wei-hongbin/CHNet
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References
Covid-19 CT lung and infection segmentation dataset. https://zenodo.org/record/3757476 (2020)
Covid-19 CT segmentation dataset. https://medicalsegmentation.com/COVID19/ (2020)
Baranchuk, D., Voynov, A., Rubachev, I., Khrulkov, V., Babenko, A.: Label-efficient semantic segmentation with diffusion models. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=SlxSY2UZQT
Bernal, J., et al.: WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comp. Med. Imaging Graph. 43, 99–111 (2015)
Byra, M., et al.: Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network. Biomed. Signal Process. Control 61, 102027 (2020)
Chen, G.P., Li, L., Dai, Y., Zhang, J.X.: NU-net: an unpretentious nested U-Net for breast tumor segmentation. arXiv preprint arXiv:2209.07193 (2022)
Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Cong, R., et al.: BCS-Net: boundary, context, and semantic for automatic COVID-19 lung infection segmentation from CT images. IEEE Trans. Instrum. Meas. 71, 1–11 (2022)
Fan, D.-P., et al.: PraNet: parallel reverse attention network for polyp segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 263–273. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_26
Fan, D.P., et al.: Inf-Net: automatic COVID-19 lung infection segmentation from CT images. IEEE Trans. Med. Imaging 39(8), 2626–2637 (2020)
Gao, Y., Zhou, M., Metaxas, D.N.: UTNet: a hybrid transformer architecture for medical image segmentation. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 61–71. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87199-4_6
Huang, H., et al.: UNet 3+: a full-scale connected UNet for medical image segmentation. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055–1059. IEEE (2020)
Jha, D., et al.: Kvasir-SEG: a segmented polyp dataset. In: Ro, Y.M., et al. (eds.) MMM 2020. LNCS, vol. 11962, pp. 451–462. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37734-2_37
Jha, D., et al.: ResUNet++: an advanced architecture for medical image segmentation. In: 2019 IEEE International Symposium on Multimedia (ISM), pp. 225–2255. IEEE (2019)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Mehta, S., Mercan, E., Bartlett, J., Weaver, D., Elmore, J.G., Shapiro, L.: Y-Net: joint segmentation and classification for diagnosis of breast biopsy images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 893–901. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_99
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), pp. 565–571. IEEE (2016)
Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
Pang, Y., Zhao, X., Xiang, T.Z., Zhang, L., Lu, H.: Zoom in and out: a mixed-scale triplet network for camouflaged object detection. In: CVPR, pp. 2160–2170 (2022)
Pang, Y., Zhao, X., Zhang, L., Lu, H.: Multi-scale interactive network for salient object detection. In: CVPR, pp. 9413–9422 (2020)
Pang, Y., Zhao, X., Zhang, L., Lu, H.: CAVER: cross-modal view-mixed transformer for bi-modal salient object detection. IEEE TIP (2023)
Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., Jagersand, M.: BASNet: boundary-aware salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7479–7489 (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Shin, S.Y., Lee, S., Yun, I.D., Kim, S.M., Lee, K.M.: Joint weakly and semi-supervised deep learning for localization and classification of masses in breast ultrasound images. IEEE Trans. Med. Imaging 38(3), 762–774 (2018)
Wei, J., Hu, Y., Zhang, R., Li, Z., Zhou, S.K., Cui, S.: Shallow attention network for polyp segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 699–708. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_66
Wei, J., Wang, S., Huang, Q.: F\(^3\)net: fusion, feedback and focus for salient object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12321–12328 (2020)
Wu, Z., et al.: Synthetic data supervised salient object detection. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 5557–5565 (2022)
Zhang, Y., et al.: DatasetGAN: efficient labeled data factory with minimal human effort. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10145–10155 (2021)
Zhao, X., et al.: M\(^2\)SNet: multi-scale in multi-scale subtraction network for medical image segmentation. arXiv preprint arXiv:2303.10894 (2023)
Zhao, X., Pang, Y., Zhang, L., Lu, H.: Joint learning of salient object detection, depth estimation and contour extraction. IEEE TIP 31, 7350–7362 (2022)
Zhao, X., Pang, Y., Zhang, L., Lu, H., Zhang, L.: Suppress and balance: a simple gated network for salient object detection. In: ECCV, pp. 35–51 (2020)
Zhao, X., Pang, Y., Zhang, L., Lu, H., Zhang, L.: Towards diverse binary segmentation via a simple yet general gated network. arXiv preprint arXiv:2303.10396 (2023)
Zhao, X., Zhang, L., Lu, H.: Automatic polyp segmentation via multi-scale subtraction network. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 120–130. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_12
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
Acknowledgements
This work was supported by the National Natural Science Foundation of China # 62276046 and the Liaoning Natural Science Foundation # 2021-KF-12-10.
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Wei, H. et al. (2024). Only Classification Head Is Sufficient for Medical Image Segmentation. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14437. Springer, Singapore. https://doi.org/10.1007/978-981-99-8558-6_25
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DOI: https://doi.org/10.1007/978-981-99-8558-6_25
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