Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation
Source abstract
Abstract
Breast cancer is one of the most prevalent cancers among women, with early detection playing a critical role in improving survival rates. This study introduces a novel transformer-based explainable model for breast cancer lesion segmentation (TEBLS), aimed at enhancing the accuracy and interpretability of breast cancer lesion segmentation in medical imaging. TEBLS integrates a multi-scale information fusion approach with a hierarchical vision transformer, capturing both local and global features by leveraging the self-attention mechanism. This model addresses the limitations of existing segmentation methods, such as the inability to effectively capture long-range dependencies and fine-grained semantic information. Additionally, TEBLS incorporates visualization techniques to provide insights into the segmentation process, enhancing the model’s interpretability for clinical use. Experiments demonstrate that TEBLS outperforms traditional and existing deep learning-based methods in segmenting complex breast cancer lesions with variations in size, shape, and texture, achieving a mean DSC of 81.86% and a mean AUC of 97.72% on the CBIS-DDSM test set. Our model not only improves segmentation accuracy but also offers a more explainable framework, which has the potential to be used in clinical settings.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · Radiomics and Machine Learning in Medical Imaging · Medical Imaging and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science, Beijing University of Technology, Beijing 100124, China
FutureNeuro Research Ireland Centre, School of Computer Science, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
School of Mathematical and Computational Sciences, Massey University, Palmerston North 0632, New Zealand
Funding
National Natural Science Foundation of China · China Scholarship Council (CSC) · Research Ireland · FutureNeuro
References (40)
Show source-provided references
1. The ever-increasing importance of cancer as a leading cause of premature death worldwide10.1002/cncr.33587 ↗
2. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries10.3322/caac.21660 ↗
3. Cancer statistics, 202310.3322/caac.21763 ↗
4. Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., and Xu, D. (2022, January 3–8). UNETR: Transformers for 3D medical image segmentation. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.10.1109/WACV51458.2022.00181 ↗
5. Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., and Patel, V.M. (October, January 27). Medical transformer: Gated axial-attention for medical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, France.
6. Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv.
7. An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on improved chimp optimization algorithm10.1016/j.eswa.2021.115651 ↗
8. Tang, J. (2010, January 16–18). A color image segmentation algorithm based on region growing. Proceedings of the 2010 2nd International Conference on Computer Engineering and Technology, Chengdu, China.
9. He, F., Mahmud, M.P., Kouzani, A.Z., Anwar, A., Jiang, F., and Ling, S.H. (2022). An improved SLIC algorithm for segmentation of microscopic cell images. Biomed. Signal Process. Control., 73.10.1016/j.bspc.2021.103464 ↗
10. Image thresholding approaches for medical image segmentation: Short literature review10.1016/j.procs.2023.01.439 ↗
11. Novel edge detection method for nuclei segmentation of liver cancer histopathology images10.1007/s12652-021-03308-4 ↗
12. Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018, January 20). U-Net++: A nested U-Net architecture for medical image segmentation. Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Granada, Spain.10.1007/978-3-030-00889-5_1 ↗
13. Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.W., and Wu, J. (2020, January 4–8). U-Net 3+: A full-scale connected U-Net for medical image segmentation. Proceedings of the ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.10.1109/ICASSP40776.2020.9053405 ↗
14. Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, 5–9 October 2015, Springer. Proceedings, Part III 18.
15. Targ, S., Almeida, D., and Lyman, K. (2016). Resnet in resnet: Generalizing residual architectures. arXiv.
16. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11–17). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF international Conference on Computer Vision, Montreal, BC, Canada.10.1109/ICCV48922.2021.00986 ↗
17. Dosovitskiy, A. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv.
18. Fan, H., Xiong, B., Mangalam, K., Li, Y., Yan, Z., Malik, J., and Feichtenhofer, C. (2021, January 11–17). Multiscale vision transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada.10.1109/ICCV48922.2021.00675 ↗
19. Explainability for large language models: A survey
20. An improved emperor penguin optimization based multilevel thresholding for color image segmentation10.1016/j.knosys.2020.105570 ↗
21. An optimum multi-level image thresholding segmentation using non-local means 2D histogram and exponential Kbest gravitational search algorithm10.1016/j.engappai.2018.03.001 ↗
22. Cigla, C., and Alatan, A.A. (2008, January 12–15). Region-based image segmentation via graph cuts. Proceedings of the 2008 15th IEEE International Conference on Image Processing, San Diego, CA, USA.10.1109/ICIP.2008.4712244 ↗
23. Convexity constrained efficient superpixel and supervoxel extraction10.1016/j.image.2015.02.005 ↗
24. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
25. Long, J., Shelhamer, E., and Darrell, T. (2015, January 7–12). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.10.1109/CVPR.2015.7298965 ↗
26. Segnet: A deep convolutional encoder-decoder architecture for image segmentation10.1109/TPAMI.2016.2644615 ↗
27. Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21–26). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.660 ↗
28. He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017, January 22–29). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.322 ↗
29. Faster R-CNN: Towards real-time object detection with region proposal networks10.1109/TPAMI.2016.2577031 ↗
30. Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2022, January 23–27). Swin-unet: Unet-like pure transformer for medical image segmentation. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.10.1007/978-3-031-25066-8_9 ↗
31. d’Ascoli, S., Touvron, H., Leavitt, M.L., Morcos, A.S., Biroli, G., and Sagun, L. (2021, January 18–24). Convit: Improving vision transformers with soft convolutional inductive biases. Proceedings of the International Conference on Machine Learning, PMLR, Virtual.
32. Huang, H., Xie, S., Lin, L., Iwamoto, Y., Han, X., Chen, Y.W., and Tong, R. (2022). ScaleFormer: Revisiting the transformer-based backbones from a scale-wise perspective for medical image segmentation. arXiv.10.24963/ijcai.2022/135 ↗
33. Zhou, H.Y., Guo, J., Zhang, Y., Yu, L., Wang, L., and Yu, Y. (2021). nnformer: Interleaved transformer for volumetric segmentation. arXiv.
34. Ji, Y., Zhang, R., Wang, H., Li, Z., Wu, L., Zhang, S., and Luo, P. (October, January 27). Multi-compound transformer for accurate biomedical image segmentation. Proceedings of the Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Part I 24, Strasbourg, France.10.1007/978-3-030-87193-2_31 ↗
35. A curated mammography data set for use in computer-aided detection and diagnosis research10.1038/sdata.2017.177 ↗
36. A survey on image data augmentation for deep learning10.1186/s40537-019-0197-0 ↗
37. Unet++: Redesigning skip connections to exploit multiscale features in image segmentation10.1109/TMI.2019.2959609 ↗
38. Vinogradova, K., Dibrov, A., and Myers, G. (2020, January 7–12). Towards interpretable semantic segmentation via gradient-weighted class activation mapping (student abstract). Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.10.1609/aaai.v34i10.7244 ↗
39. Aliniya, P., Nicolescu, M., Nicolescu, M., and Bebis, G. (2023, January 16–18). Hybrid Region and Pixel-Level Adaptive Loss for Mass Segmentation on Whole Mammography Images. Proceedings of the International Symposium on Visual Computing, Lake Tahoe, NV, USA.10.1007/978-3-031-47969-4_1 ↗
40. AU-Net: A deep learning network for precise water body extraction in the middle and lower reaches of the yellow river