To enable deep learning-based computer-aided diagnosis to achieve excellent performance in differentiating benign and malignant breast tumors in ultrasound images, a large number of labeled training samples must be collected. However, it is difficult to acquire sufficient samples due to the high costs of data collection and labeling. Fortunately, breast ultrasound images have two labels from different sources of domain knowledge: the biopsy results are “clean” labels, and the Breast Imaging Reporting and Data System (BI-RADS) score functions as a “noisy” label. Based on these two label types, we propose a multitask classification method based on label distribution correction (MTLC-Net). In our method, we propose different tasks to address the noisy and clean labels. Specifically, we propose a label distribution correction task for noisy labels that includes jointly training the network parameters and soft labels. The model is generalizable and robust by correcting the noisy label distribution based on the BI-RADS score, and it extracts knowledge from the noisy label task to improve the learning in the clean-label task. We conducted extensive comparisons with existing methods. Our method achieved a classification accuracy of 75.8%, a precision of 73.0%, a recall of 80.1% and an F1 score of 0.764—results that are significantly better than those of the existing state-of-the-art methods for differentiating benign and malignant breast tumors in ultrasound images.
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This research was funded by the National Natural Science Foundation of China under Grant Numbers 61572109 and 61772006, the Science and Technology Program of Guangxi under Grant Number AB17129012, the Science and Technology Major Project of Guangxi under Grant Number AA17204096, the Special Fund for Scientific and Technological Bases and Talents of Guangxi under Grant Number 2016AD05050, and the Special Fund for Bagui Scholars of Guangxi.
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Cao, Z., Yang, G., Li, X. et al. Multitask Classification Method Based on Label Correction for Breast Tumor Ultrasound Images. Neural Process Lett (2021). https://doi.org/10.1007/s11063-021-10455-4
- Deep learning
- Multitask learning
- Breast ultrasound image
- Noisy label