Researching | Hierarchical Transformer with Multi-Scale Parallel Aggregation for Breast Tumor Segmentation

Laser & Optoelectronics Progress, Volume. 62, Issue 2, 0217001(2025)

Hierarchical Transformer with Multi-Scale Parallel Aggregation for Breast Tumor Segmentation

Ping Xia^1,2、*, Yudie Wang^1,2, Bangjun Lei^1,2, Cheng Peng^1,2, Guangyi Zhang^1,2, and Tinglong Tang^1,2

Author Affiliations

¹Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, Three Gorges University, Yichang 443002, Hubei , China

²College of Computer and Information Technology, Three Gorges University, Yichang 443002, Hubei , China

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[1] Sung H, Ferlay J, Siegel R L et al[J]. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, 71, 209-249(2021).

[2] Siegel R L, Miller K D, Fuchs H E et al[J]. Cancer statistics, 2022, 72, 7-33(2022).

[3] Zheng R S, Zhang S W, Zeng H M et al. Cancer incidence and mortality in China in 2016[J]. Journal of the National Cancer Center, 2, 1-9(2022).

[4] Bleicher R J, Ruth K, Sigurdson E R et al. Time to surgery and breast cancer survival in the United States[J]. JAMA Oncology, 2, 330-339(2016).

[5] Ginsburg O, Yip C H, Brooks A et al. Early detection of breast cancer: a phased approach to implementation[J]. Cancer, 126, 2379-2393(2020).

[6] Sood R, Rositch A F, Shakoor D et al. Ultrasonography for breast cancer detection: a systematic review and meta-analysis[J]. Journal of Global Oncology, 5, 1-17(2019).

[7] Yang S L, Gao X C, Liu L W et al. Performance and reading time for automated breast US with and without computer-aided detection[J]. Radiology, 292, 540-549(2019).

[8] Huang Q H, Zhang F, Li X L. Machine learning in ultrasound computer-aided diagnostic systems: a survey[J]. BioMed Res International, 2018, 5137904(2018).

[9] Hupse R, Samulski M, Lobbes M B et al. Computer-aided detection of masses on mammography: interactive decision support versus prompts[J]. Radiology, 266, 123-129(2013).

[10] Sanders D R, Vukich J A, Doney K et al. U.S. food and drug administration clinical trial of implantable contact lenses for moderate-to-high myopia[J]. Ophthalmology, 110, 255-266(2003).

[11] Karunanayake N, Aimmanee P, Lohitvisate W et al. Particle method for the segmentation of breast tumors in ultrasound images[J]. Mathematics and Computers in Simulation, 170, 257-284(2020).

[12] Mughal B, Muhammad N, Sharif M. Adaptive hysteresis thresholding segmentation technique for localizing breast masses in the curve-stitching domain[J]. International Journal of Medical Informatics, 126, 26-34(2019).

[13] Chan T F, Vese L. Active contour and segmentation models using geometric PDE’s for medical imaging[M]. Geometric methods in biomedical image processing, 63-75(2002).

[14] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]. Medical image computing and computer-assisted intervention MICCAI 2015, 9351, 234-241(2015).

[15] Zhang H, Qiu D W, Feng Y B et al. Improved U-Net models and their applications in medical image segmentation: a review[J]. Laser Optoelectronics Progress, 59, 0200005(2022).

[16] Almajalid R, Shan J, Du Y D et al. Development of a deep learning-based method for breast ultrasound image segmentation[C], 1103-1108(2018).

[17] Zhou Z W, Siddiquee M R, Tajbakhsh N et al. UNet++: nested U-Net architecture for medical image segmentation[M]. Deep learning in medical image analysis and multimodal learning for clinical decision support, 11045, 3-11(2018).

[18] Mnih V, Heess N, Graves A et al. Recurrent models of visual attention[EB/OL]. https://arxiv.org/abs/1406.6247

[19] Zhao F, Zhong B B, Liu H Q. Multi-scale residual U-Net fundus blood vessel segmentation based on attention mechanism[J]. Laser Optoelectronics Progress, 59, 1810002(2022).

[20] Oktay O, Schlemper J, le Folgoc L et al. Attention U-Net: learning where to locate the pancreas[EB/OL]. https://arxiv.org/abs/1804.03999

[21] Zhuang Z M, Li N, Raj A N J et al. RDAU-NET model for lesion segmentation in breast ultrasound images[J]. PLoS One, 14, e0221535(2019).

[22] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image worth[EB/OL], 16-16. https://arxiv.org/abs/2010.11929

[23] Hao W Y, Cai H Y, Zuo T T et al. Intravascular ultrasound image segmentation using transformer branches and topology enforcement[J]. Laser Optoelectronics Progress, 61, 1237008(2024).

[24] Chen J N, Lu Y Y, Yu Q H et al. TransUNet: transformers are strong encoders used for medical image segmentation[EB/OL]. https://arxiv.org/abs/2102.04306

[25] Zhang Y D, Liu H Y, Hu Q. TransFuse: fusing transformers and CNNs for medical image segmentation[EB/OL]. https://arxiv.org/abs/2102.08005

[26] Zhou B L, Zhao H, Puig X et al. Semantic understanding of scenes using ADE20K dataset[J]. International Journal of Computer Vision, 127, 302-321(2019).

[27] He K M, Zhang X Y Zhang, Ren S Q et al. Deep residual learning for image recognition[EB/OL]. https://arxiv.org/abs/1512.03385

[28] Zhang Q L, Yang YB. SA-Net: shuffle attention for deep convolutional neural networks[C], 2235-2239(2021).

[29] Qiu Z H, Wang Z C, Zhang M et al. BDG-Net: boundary distribution-guided network for accurate polyp segmentation[EB/OL]. https://arxiv.org/abs/2201.00767

[30] Xie E Z, Wang W H et al. SegFormer: a simple and efficient design for semantic segmentation with transformers[J]. Advances in Neural Information Processing Systems, 34, 12077-12090(2021).

[31] Park J, Woo S, Lee J Y et al. Bam: bottleneck attention module[EB/OL]. https://arxiv.org/abs/2010.11929

[32] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[EB/OL]. https://arxiv.org/abs/1807.06521

[33] Ma N N, Zhang X Y, Zheng H T et al. ShuffleNet V2: practical guidelines for efficient CNN architectural design[EB/OL]. https://arxiv.org/abs/1807.11164

[34] Al-Dhabyani W, Gomaa M, Khaled H et al. Breast ultrasonography image dataset[J]. Data in Brief, 28, 104863(2020).

[35] Xu M, Huang K, Chen Q et al. Mssa-Net: multi-scale self-attention network for breast ultrasound image segmentation[C], 827-831(2021).

[36] Yap M H, Goyal M, Osman F et al. Breast ultrasound region of interest detection and lesion localization[J]. Artificial Intelligence in Medicine, 107, 101880(2020).

[37] Huang Q H, Huang Y H, Luo Y Z et al. Segmentation of breast-ultrasound images with semantic classification of superpixels[J]. Medical Image Analysis, 61, 101657(2020).

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Ping Xia, Yudie Wang, Bangjun Lei, Cheng Peng, Guangyi Zhang, Tinglong Tang. Hierarchical Transformer with Multi-Scale Parallel Aggregation for Breast Tumor Segmentation[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0217001

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Paper Information

Category: Medical Optics and Biotechnology

Received: Mar. 6, 2024

Accepted: Apr. 25, 2024

Published Online: Jan. 9, 2025

The Author Email:

DOI:10.3788/LOP240836

CSTR:32186.14.LOP240836

Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology