A joint distillation model for the tumor segmentation using breast ultrasound images.
10.7507/1001-5515.202311054
- Author:
Hongjiang GUO
1
;
Youyou DING
1
;
Hao DANG
1
;
Tongtong LIU
1
;
Xuekun SONG
1
;
Ge ZHANG
1
;
Shuo YAO
1
;
Daisen HOU
1
;
Zongwang LYU
2
Author Information
1. School of Information Technology, Henan University of Chinese Medicine, Zhengzhou 450046, P. R. China.
2. Institute for Complexity Science, Henan University of Technology, Zhengzhou 450001, P. R. China.
- Publication Type:Journal Article
- Keywords:
Attention mechanism;
Breast ultrasound images;
Joint distillation;
Semantic segmentation;
U-Net
- MeSH:
Humans;
Breast Neoplasms/diagnostic imaging*;
Female;
Ultrasonography, Mammary/methods*;
Image Processing, Computer-Assisted/methods*;
Algorithms;
Neural Networks, Computer;
Breast/diagnostic imaging*
- From:
Journal of Biomedical Engineering
2025;42(1):148-155
- CountryChina
- Language:Chinese
-
Abstract:
The accurate segmentation of breast ultrasound images is an important precondition for the lesion determination. The existing segmentation approaches embrace massive parameters, sluggish inference speed, and huge memory consumption. To tackle this problem, we propose T 2KD Attention U-Net (dual-Teacher Knowledge Distillation Attention U-Net), a lightweight semantic segmentation method combined double-path joint distillation in breast ultrasound images. Primarily, we designed two teacher models to learn the fine-grained features from each class of images according to different feature representation and semantic information of benign and malignant breast lesions. Then we leveraged the joint distillation to train a lightweight student model. Finally, we constructed a novel weight balance loss to focus on the semantic feature of small objection, solving the unbalance problem of tumor and background. Specifically, the extensive experiments conducted on Dataset BUSI and Dataset B demonstrated that the T 2KD Attention U-Net outperformed various knowledge distillation counterparts. Concretely, the accuracy, recall, precision, Dice, and mIoU of proposed method were 95.26%, 86.23%, 85.09%, 83.59%and 77.78% on Dataset BUSI, respectively. And these performance indexes were 97.95%, 92.80%, 88.33%, 88.40% and 82.42% on Dataset B, respectively. Compared with other models, the performance of this model was significantly improved. Meanwhile, compared with the teacher model, the number, size, and complexity of student model were significantly reduced (2.2×10 6 vs. 106.1×10 6, 8.4 MB vs. 414 MB, 16.59 GFLOPs vs. 205.98 GFLOPs, respectively). Indeedy, the proposed model guarantees the performances while greatly decreasing the amount of computation, which provides a new method for the deployment of clinical medical scenarios.
