Details

Autor(en) / Beteiligte

• Ding, Wanli
• Zhang, Heye
• Zhuang, Shuxin
• Zhuang, Zhemin
• Gao, Zhifan

Titel

Multi-view stereoscopic attention network for 3D tumor classification in automated breast ultrasound

Ist Teil von

• Expert systems with applications, 2023-12, Vol.234, p.120969, Article 120969

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• As the new generation of breast cancer screening tools, automated breast ultrasound (ABUS) has some difficulties of the long interpretation time in clinical application. A computer-aided diagnosis algorithm can assist diagnosticians in locating and classifying benign and malignant tumors in ABUS images quickly. However, locating and classifying ABUS images is challenging because they have inapparent and small lesion regions and blurry discriminative regions. This paper proposes the multi-view stereoscopic attention network (MVSA-Net) to address the challenges. First, MVSA-Net crops the three-dimensional (3D) lesion regions through the 3D localization unit. Second, MVSA-Net classifies the cropped lesion regions through the classification unit. The localization unit can pay more attention to lesion regions by constructing the stereoscopic attention module. Besides, the localization unit constructs the 3D localization tensor through the split output design. The classification unit can extract the stereoscopic features through the stereoscopic view and the plane features through the plane view based on the Transformer network. It also fuses the two features through the level connection design. Besides, it can more focus on the borderline region of the tumor based on the region-prior module. The paper uses 172 ABUS images to train MVSA-Net, and 42 ABUS images to test MVSA-Net. On the test set, the accuracy and AUC of MVSA-Net are 95.24% and 99.76%, respectively. MVSA-Net can assist diagnosticians in decreasing the interpretation time of ABUS images, thus helping more people screen breast tumors earlier.