Presented at the 17th International Workshop on Breast Imaging (IWBI 2024), Chicago.

Work by Zhikai Yang, Tianyu Fan, Örjan Smedby, and Rodrigo Moreno.

Abstract

3D breast ultrasound is a radiation-free and effective imaging technology for breast-tumour diagnosis, but checking the 3D volume is time-consuming compared to mammograms.

We propose a 2.5D deep-learning–based breast-ultrasound tumour classification system. First, the pre-trained STU-Net is fine-tuned and used to segment the tumour in 3D. Then, DenseNet-121 is fine-tuned for classification using the 10 slices with the largest tumoral area and their adjacent slices.

The Tumour Detection, Segmentation and Classification on Automated 3D Breast Ultrasound (TDSC-ABUS) MICCAI Challenge 2023 dataset was used to train and validate the proposed method. Compared to a 3D convolutional neural network and radiomics baselines, the proposed method achieves better performance.

• Paper: SPIE Digital Library — DOI 10.1117/12.3025534