A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images

Abstract

Breast cancer is one of the most fatal diseases leading to the death of several women across the world. But early diagnosis of breast cancer can help to reduce the mortality rate. So an efficient multi-task learning approach is proposed in this work for the automatic segmentation and classification of breast tumors from ultrasound images. The proposed learning approach consists of an encoder, decoder, and bridge blocks for segmentation and a dense branch for the classification of tumors. For efficient classification, multi-scale features from different levels of the network are used. Experimental results show that the proposed approach is able to enhance the accuracy and recall of segmentation by $1.08\%$, $4.13\%$, and classification by $1.16\%$, $2.34\%$, respectively than the methods available in the literature.

Keywords: benign; breast cancer; classification; malignant; multi-task learning; segmentation.

Figure 1.

Difference between the neural layers module in standard UNet and the proposed residual module in the this work: (a) Neural layers used in U-Net and (b) proposed residual module.

Figure 2.

The proposed multi-task learning approach for segmentation and classification.

Figure 3.

Comparison of segmentation performance reported by the proposed model with other segmentation methods.