CTG-Net: Cross-task guided network for breast ultrasound diagnosis.

ABSTRACT: Deep learning techniques have achieved remarkable success in lesion segmentation and classification between benign and malignant tumors in breast ultrasound images. However, existing studies are predominantly focused on devising efficient neural network-based learning structures to tackle specific tasks individually. By contrast, in clinical practice, sonographers perform segmentation and classification as a whole; they investigate the border contours of the tissue while detecting abnormal masses and performing diagnostic analysis. Performing multiple cognitive tasks simultaneously in this manner facilitates exploitation of the commonalities and differences between tasks. Inspired by this unified recognition process, this study proposes a novel learning scheme, called the cross-task guided

SUBMITTER: Yang K 

PROVIDER: S-EPMC9371312 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature