Joint localization and classification of breast tumors on ultrasound images using a novel auxiliary attention-based framework

TL;DR

This paper introduces a novel attention-based framework with semi-supervised learning for improved joint localization and classification of breast tumors in ultrasound images, reducing data annotation needs and enhancing performance.

Contribution

The study proposes a modular, attention-driven model with semi-supervised training for breast tumor detection, addressing limitations of existing methods in information sharing and data annotation.

Findings

Improved localization and classification accuracy on two datasets.

Effective semi-supervised learning with incomplete annotations.

Flexible framework adaptable to various applications.

Abstract

Automatic breast lesion detection and classification is an important task in computer-aided diagnosis, in which breast ultrasound (BUS) imaging is a common and frequently used screening tool. Recently, a number of deep learning-based methods have been proposed for joint localization and classification of breast lesions using BUS images. In these methods, features extracted by a shared network trunk are appended by two independent network branches to achieve classification and localization. Improper information sharing might cause conflicts in feature optimization in the two branches and leads to performance degradation. Also, these methods generally require large amounts of pixel-level annotated data for model training. To overcome these limitations, we proposed a novel joint localization and classification model based on the attention mechanism and disentangled semi-supervised learning strategy. The model used in this study is composed of a classification network and an auxiliary lesion-aware network. By use of the attention mechanism, the auxiliary lesion-aware network can optimize multi-scale intermediate feature maps and extract rich semantic information to improve classification and localization performance. The disentangled semi-supervised learning strategy only requires incomplete training datasets for model training. The proposed modularized framework allows flexible network replacement to be generalized for various applications. Experimental results on two different breast ultrasound image datasets demonstrate the effectiveness of the proposed method. The impacts of various network factors on model performance are also investigated to gain deep insights into the designed framework.