Ultrasound Image Lesion Detection Algorithm Optimized by Feature Feedback Mechanism

DING Jianrui; WANG Lingtao; TANG Fenghe; NING Chunping

doi:10.11999/JEIT230385

Volume 46 Issue 3

Mar. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(3): 1013-1021

DING Jianrui, WANG Lingtao, TANG Fenghe, NING Chunping. Ultrasound Image Lesion Detection Algorithm Optimized by Feature Feedback Mechanism[J]. Journal of Electronics & Information Technology, 2024, 46(3): 1013-1021. doi: 10.11999/JEIT230385

Citation:

DING Jianrui, WANG Lingtao, TANG Fenghe, NING Chunping. Ultrasound Image Lesion Detection Algorithm Optimized by Feature Feedback Mechanism[J]. Journal of Electronics & Information Technology, 2024, 46(3): 1013-1021. doi: 10.11999/JEIT230385

Citation:

PDF( 5710 KB)

Ultrasound Image Lesion Detection Algorithm Optimized by Feature Feedback Mechanism

doi: 10.11999/JEIT230385

1.
School of Computer Science and Technology, Harbin Institute of Technology (Weihai), Weihai 264209, China
2.
Ultrasound Department, The Affiliated Hospital of Qingdao University, Qingdao 266003, China

Funds: The National Natural Science Foundation of China (U22A2033), The Natural Science Foundation of Shandong Province (ZR2020MH290)

Received Date: 2023-05-08
Accepted Date: 2023-08-21
Rev Recd Date: 2023-08-18

Available Online: 2023-08-24

Publish Date: 2024-03-27

Abstract

Abstract

A lesion detection method in ultrasound images based on feature feedback mechanism is proposed to realize real-time accurate localization and detection of ultrasound lesions. The proposed method consists of two parts: feature extraction network based on feature feedback mechanism and adaptive detection head based on divide-and-conquer strategy. The feature feedback network fully learns the global context information and local low-level semantic details of ultrasound images through feedback feature selection and weighted fusion calculation to improve the recognition ability of local lesion features. The adaptive detection head performs divide-and-conquer preprocessing on the multi-level features extracted by the feature feedback network. By combining physiological prior knowledge and feature convolution, adaptive modeling of lesion shape and scale features is performed on features at all levels to enhance the detection effect of the detection head on lesions of different sizes under multi-level features. The proposed method is tested on the thyroid ultrasound image dataset, and 70.3% AP, 99.0% AP50 and 88.4% AP75 are obtained. Experimental results show that the proposed algorithm can achieve more accurate real-time detection and positioning of ultrasound image lesions in comparison with mainstream detection algorithm.
- Lesion detection,
- Feature feedback,
- Adaptive detection head

FullText(HTML)

References(27)

References

[1]	WATAYA T, YANAGAWA M, TSUBAMOTO M, et al. Radiologists with and without deep learning–based computer-aided diagnosis: Comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses[J]. European Radiology, 2023, 33(1): 348–359. doi: 10.1007/s00330-022-08948-4.
[2]	SOLYMOSI T, HEGEDŰS L, BONNEMA S J, et al. Considerable interobserver variation calls for unambiguous definitions of thyroid nodule ultrasound characteristics[J]. European Thyroid Journal, 2023, 12(2): e220134. doi: 10.1530/ETJ-22-0134.
[3]	YAP M H, GOYAL M, OSMAN F, et al. Breast ultrasound region of interest detection and lesion localisation[J]. Artificial Intelligence in Medicine, 2020, 107: 101880. doi: 10.1016/j.artmed.2020.101880.
[4]	LI Yujie, GU Hong, WANG Hongyu, et al. BUSnet: A deep learning model of breast tumor lesion detection for ultrasound images[J]. Frontiers in Oncology, 2022, 12: 848271. doi: 10.3389/fonc.2022.848271.
[5]	MENG Hui, LIU Xuefeng, NIU Jianwei, et al. DGANet: A dual global attention neural network for breast lesion detection in ultrasound images[J]. Ultrasound in Medicine and Biology, 2023, 49(1): 31–44. doi: 10.1016/j.ultrasmedbio.2022.07.006.
[6]	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 580–587.
[7]	REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149. doi: 10.1109/TPAMI.2016.2577031.
[8]	LIANG Tingting, CHU Xiaojie, LIU Yudong, et al. CBNet: A composite backbone network architecture for object detection[J]. IEEE Transactions on Image Processing, 2022, 31: 6893–6906. doi: 10.1109/TIP.2022.3216771.
[9]	QIAO Siyuan, CHEN L C, and YUILLE A. DetectoRS: Detecting objects with recursive feature pyramid and switchable atrous convolution[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 10208–10219.
[10]	LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]. 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 2999–3007.
[11]	REDMON J and FARHADI A. YOLOv3: An incremental improvement[J]. arXiv preprint arXiv: 1804.02767, 2018.
[12]	TIAN Zhi, SHEN Chunhua, CHEN Hao, et al. FCOS: Fully convolutional one-stage object detection[C]. 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 2019: 9626–9635.
[13]	ZHANG Haoyang, WANG Ying, DAYOUB F, et al. VarifocalNet: An IoU-aware dense object detector[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 8510–8519.
[14]	CHEN Qiang, WANG Yingming, YANG Tong, et al. You only look one-level feature[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 13034–13043.
[15]	TAN Mingxing, PANG Ruoming, and LE Q V. EfficientDet: Scalable and efficient object detection[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 10778–10787.
[16]	GE Zheng, LIU Songtao, WANG Feng, et al. YOLOX: Exceeding YOLO series in 2021[J]. arXiv preprint arXiv: 2107.08430, 2021.
[17]	WANG C Y, BOCHKOVSKIY A, and LIAO H Y M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[J]. arXiv preprint arXiv: 2207.02696, 2022.
[18]	WANG Wen, ZHANG Jing, CAO Yang, et al. Towards data-efficient detection transformers[C]. The 17th European Conference on Computer Vision, Tel Aviv, Israel, 2022: 88–105.
[19]	CHEN Xiangyu, HU Qinghao, LI Kaidong, et al. Accumulated trivial attention matters in vision transformers on small datasets[C]. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, USA, 2023: 3973–3981.
[20]	CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers[C]. Proceedings of the 16th European Conference on Computer Vision, Glasgow, UK, 2020: 213–229.
[21]	LIU Shilong, LI Feng, ZHANG Hao, et al. DAB-DETR: Dynamic anchor boxes are better queries for DETR[C]. The Tenth International Conference on Learning Representations (Virtual), 2022: 1–20. doi: 10.48550/arXiv.2201.12329.
[22]	ZHANG Hao, LI Feng, LIU Shilong, et al. DINO: DETR with improved DeNoising anchor boxes for end-to-end object detection[C]. The Eleventh International Conference on Learning Representations, Kigali, Rwanda, 2023: 1–19. doi: 10.48550/arXiv.2203.03605.
[23]	HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778.
[24]	LIU Zhuang, MAO Hanzi, WU Chaoyuan, et al. A ConvNet for the 2020s[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 11966–11976.
[25]	PENG Zhiliang, GUO Zonghao, HUANG Wei, et al. Conformer: Local features coupling global representations for recognition and detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(8): 9454–9468. doi: 10.1109/TPAMI.2023.3243048.
[26]	WANG W, DAI J, CHEN Z, et al. Internimage: Exploring large-scale vision foundation models with deformable convolutions[C]. IEEE Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, 2023: 14408–14419.
[27]	SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization[C]. 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 618–626.