@inproceedings{641e35e3d6b84d8ba07be074323c29f0,
title = "Breast Lesions Segmentation using Dual-level UNet (DL-UNet)",
abstract = "Breast disease is one of the primary diseases endangering women's health. Accurate segmentation of breast lesions can help doctors diagnose breast diseases. However, the size and morphology of breast lesions are different, and the intensity of breast tissue is uneven. Thus, it is challenging to segment the lesion area accurately. In this paper, we propose Dual-scale Feature Fusion (DSFF) module and Edgeloss to segment breast lesions. The DSFF module aims to integrate two-scale features and design another effective skip connection scheme to reduce false positive regions. To solve the problem of unclear segmentation boundary, we design Edgeloss for additional supervision on the boundary region to obtain a finer segmentation boundary. The experiment results show that the proposed DL-UNet with the DSFF module and new Edgeloss performs best in several classic networks.",
keywords = "Medical image segmentation, boundary-based loss, breast lesions, skip connection",
author = "Yanjiao Zhao and Zhihui Lai and Linlin Shen and Heng Kong",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 35th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2022 ; Conference date: 21-07-2022 Through 23-07-2022",
year = "2022",
doi = "10.1109/CBMS55023.2022.00067",
language = "English",
series = "Proceedings - IEEE Symposium on Computer-Based Medical Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "339--344",
editor = "Linlin Shen and Gonzalez, {Alejandro Rodriguez} and KC Santosh and Zhihui Lai and Rosa Sicilia and Almeida, {Joao Rafael} and Bridget Kane",
booktitle = "Proceedings - 2022 IEEE 35th International Symposium on Computer-Based Medical Systems, CBMS 2022",
address = "United States",
}