Page 91 - AIH-2-2

Artificial Intelligence in Health Efficient knowledge distillation for breast US

classification through learning from noisy labeled Using Mask R-CNN. In: 2019 IEEE International Ultrasonics
ultrasound images. Med Phys. 2020;47:1048-1057. Symposium (IUS). IEEE; 2019. p. 1470-1472.
doi: 10.1002/mp.13966 doi: 10.1109/ULTSYM.2019.8926185
44. Lee K, Lee H, El Fakhri G, Woo J, Hwang JY. Self-Supervised 53. He K, Gkioxari G, Doll’ar P, Girshick R. Mask R-CNN.
Domain Adaptive Segmentation of Breast Cancer Via Test- In: Proceedings of the IEEE International Conference on
Time Fine-Tuning. In: International Conference on Medical Computer Vision; 2017. p. 2961-2969.
Image Computing and Computer Assisted Intervention. doi: 10.48550/arXiv.1703.06870
Springer; 2023. p. 539-550.
54. Amiri M, Brooks R, Behboodi B, Rivaz H. Two-stage
doi: 10.1007/978-3-031-43907-0_52 ultrasound image segmentation using u-net and test
45. Ronneberger O, Fischer P, Brox T. U-net: Convolutional time augmentation. Int J Comput Assist Radiol Surg.
Networks for Biomedical Image Segmentation. In: 2020;15:981-988.
International Conference on Medical Image Computing and doi: 10.1007/s11548-020-02158-3
Computer-assisted Intervention. Springer; 2015. p. 234-241.
55. Lee H, Park J, Hwang JY. Channel attention module
doi: 10.1007/978-3-319-24574-4_28 with multiscale grid average pooling for breast cancer
46. Fan J, Liu D, Chang H, Huang H, Chen M, Cai W. Taxonomy segmentation in an ultrasound image. IEEE Trans Ultrason
Adaptive Cross-domain adaptation in Medical Imaging Via Ferroelectr Freq Control. 2020;67:1344-1353.
Optimization Trajectory Distillation. In: Proceedings of the doi: 10.1109/TUFFC.2020.2972573
IEEE/CVF International Conference on Computer Vision;
2023. p. 21174-21184. 56. Shareef B, Xian M, Vakanski A. Stan: Small Tumor-aware
Network for Breast Ultrasound Image Segmentation. In: 2020
doi: 10.48550/arXiv.2307.14709 IEEE 17 International Symposium on Biomedical Imaging
th
47. Yap MH, Goyal M, Osman F, et al. End-to-end breast (ISBI). IEEE; 2020. p. 1-5.
ultrasound lesions recognition with a deep learning approach. doi: 10.1109/ISBI45749.2020.9098691
In: Medical Imaging 2018: Biomedical Applications in
Molecular, Structural, and Functional Imaging. In: Proceeding 57. Shareef B, Vakanski A, Xian M, Freer PE. ESTAN: Enhanced
International Society for Optics and Photonics. Vol. 10578; small tumor-aware network for breast ultrasound image
2018. p. 1057819. segmentation, arXiv preprint arXiv:2009.12894; 2020.
doi: 10.1117/12.2293498 58. Singh VK, Abdel-Nasser M, Akram F, et al. Breast tumor
segmentation in ultrasound images using contextual-
48. Long J, Shelhamer E, Darrell T. Fully Convolutional Networks information-aware deep adversarial learning framework.
for Semantic Segmentation. In: Proceedings of the IEEE Expert Syst Appl. 2020;162:113870.
Conference on Computer Vision and Pattern Recognition;
2015. p. 3431-3440. doi: 10.1016/j.eswa.2020.113870
doi: 10.1109/CVPR.2015.7298965 59. Mirza M, Osindero S. Conditional generative adversarial
nets. arXiv preprint arXiv:1411.1784; 2014.
49. Abraham N, Khan NM. A Novel Focal Tversky Loss Function
with Improved Attention u-net for Lesion Segmentation. In: doi: 10.48550/arXiv.1411.1784
2019 IEEE 16 International Symposium on Biomedical 60. Chen LC, Papandreou G, Schroff F, Adam H. Rethinking
th
Imaging (ISBI 2019). IEEE; 2019. p. 683-687. atrous convolution for semantic image segmentation. arXiv
doi: 10.1109/ISBI.2019.8759329 preprint arXiv:1706.05587; 2017.
50. Zhuang Z, Li N, Joseph Raj AN, Mahesh VG, Qiu S. doi: 10.48550/arXiv.1706.05587
An RDAU-net model for lesion segmentation in breast 61. Fu J, Liu J, Tian H, et al. Dual Attention Network for Scene
ultrasound images. PLoS One. 2019;14:e0221535. Segmentation. In: Proceedings of the IEEE/CVF Conference
doi: 10.1371/journal.pone.0221535 on Computer Vision and Pattern Recognition; 2019.
p. 3146-3154.
51. Costa MGF, Campos JBM, de Aquino e Aquino G, de
Albuquerque Pereira WC, Costa Filho CFF. Evaluating the doi: 10.1109/CVPR.2019.00326
performance of convolutional neural networks with direct 62. Hu J, Shen L, Sun G. Squeeze-and-Excitation Networks. In:
acyclic graph architectures in automatic segmentation of Proceedings of the IEEE Conference on Computer Vision and
breast lesion in us images. BMC Med Imaging. 2019;19:1-13. Pattern Recognition; 2018. p. 7132-7141.
doi: 10.1186/s12880-019-0389-2 doi: 10.48550/arXiv.1709.01507
52. Liang Y, He R, Li Y, Wang Z. Simultaneous Segmentation 63. Hussain S, Xi X, Ullah I, et al. Contextual level-set
and Classification of Breast Lesions from Ultrasound Images method for breast tumor segmentation. IEEE Access.

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