Page 92 - AIH-2-2

Artificial Intelligence in Health Efficient knowledge distillation for breast US

2020;8:189343-189353. 72. Yeung M, Sala E, Schonlieb CB, Rundo L. Unified focal loss:
Generalising dice and cross entropy-based losses to handle
doi: 10.1109/ACCESS.2020.3029684
class imbalanced medical image segmentation. Comput Med
64. Qu X, Shi Y, Hou Y, Jiang J. An attention-supervised full- Imaging Graph. 2022;95:102026.
resolution residual network for the segmentation of breast
ultrasound images. Med Phys. 2020;47:5702-5714. doi: 10.1016/j.compmedimag.2021.102026
73. Xu C, Qi Y, Wang Y, Lou M, Pi J, Ma Y. ARF-Net: An adaptive
doi: 10.1002/mp.14470
receptive field network for breast mass segmentation in
65. Pohlen T, Hermans A, Mathias M, Leibe B. Full-Resolution whole mammograms and ultrasound images. Biomed Signal
Residual Networks for Semantic Segmentation in Street Process Control. 2022;17:103178.
Scenes. In: Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition; 2017. p. 4151-4160. doi: 10.1016/j.bspc.2021.103178
74. Lou M, Meng J, Qi Y, Li X, Ma Y. MCRNet: Multi-level context
doi: 10.48550/arXiv.1611.08323
refinement network for semantic segmentation in breast
66. Ning Z, Wang K, Zhong S, Feng Q, Zhang Y. Cf2-net: Coarse- ultrasound imaging. Neurocomputing. 2022;470:154-169.
to-fine fusion convolutional network for breast ultrasound 75. Yang K, Suzuki A, Ye J, Nosato H, Izumori A, Sakanashi H.
image segmentation, arXiv preprint arXiv:2003.10144; 2020.
CTG-Net: Cross-task guided network for breast ultrasound
doi: 10.48550/arXiv.2003.10144 diagnosis. PLoS One. 2022;17:e0271106.
67. Behboodi B, Amiri M, Brooks R, Rivaz H. Breast Lesion doi: 10.1016/j.neucom.2021.10.102
Segmentation in Ultrasound Images with Limited Annotated 76. Xie S, Girshick R, Dollar P, Tu Z, He K. Aggregated Residual
Data. In: 2020 IEEE 17 International Symposium on Transformations for Deep Neural Networks. In: Proceedings
th
Biomedical Imaging (ISBI). IEEE; 2020. p. 1834-1837.
of the IEEE Conference on Computer Vision and Pattern
doi: 10.1109/ISBI45749.2020.9098685 Recognition; 2017. p. 1492-1500.
68. Gao C, Ye H, Cao F, Wen C, Zhang Q, Zhang F. Multiscale doi: 10.48550/arXiv.1611.05431
fused network with additive channel-spatial attention for 77. Howard A, Sandler M, Chu G, et al. Searching for
image segmentation. Knowl Based Syst. 2021;214:106754.
Mobilenetv3. In: Proceedings of the IEEE/CVF International
doi: 10.1016/j.knosys.2021.106754 Conference on Computer Vision; 2019. p. 1314-1324.
69. Su R, Zhang D, Liu J, Cheng C. MSU-Net: Multi-scale doi: 10.48550/arXiv.1905.02244
u-net for 2D medical image segmentation. Front Genet. 78. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet:
2021;12:140.
A Large-Scale Hierarchical Image Database. In: 2009 IEEE
doi: 10.3389/fgene.2021.639930 Conference on Computer Vision and Pattern Recognition.
IEEE; 2009. p. 248-255.
70. Xu M, Huang K, Chen Q, Qi X. MSSA-Net: Multi-scale Self-
attention Network for Breast Ultrasound Image Segmentation. doi: 10.1109/CVPR.2009.5206848
In: 2021 IEEE 18 International Symposium on Biomedical 79. Iakubovskii P. Segmentation Models Pytorch; 2019. Available
th
Imaging (ISBI). IEEE; 2021. p. 827-831.
from: https://github.com/qubvel/segmentation_models.
doi: 10.1109/ISBI48211.2021.9433899 pytorch [Last accessed on 2024 Dec 19].
71. Huang H, Chen H, Xu H, et al. Cross-tissue/organ transfer 80. Buslaev A, Iglovikov VI, Khvedchenya E, Parinov A,
learning for the segmentation of ultrasound images using Druzhinin M, Kalinin AA. Albumentations: Fast and
deep residual u-net. J Med Biol Eng. 2021;41:137-145. flexible image augmentations. Information. 2020;11:125.
doi: 10.1007/s40846-020-00585-w doi: 10.3390/info11020125

Volume 2 Issue 2 (2025) 86 doi: 10.36922/aih.3509