3D breast ultrasound image classification using 2.5D deep learning

Zhikai Yang; Tianyu Fan; Örjan Smedby; Rodrigo Moreno

doi:10.1117/12.3025534

29 May 2024 3D breast ultrasound image classification using 2.5D deep learning

Zhikai Yang, Tianyu Fan, Örjan Smedby, Rodrigo Moreno

Proceedings Volume 13174, 17th International Workshop on Breast Imaging (IWBI 2024); 131741R (2024) https://doi.org/10.1117/12.3025534
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States

Abstract

The 3D breast ultrasound is a radiation-free and effective imaging technology for breast tumor diagnosis. However, checking the 3D breast ultrasound is time-consuming compared to mammograms. To reduce the workload of radiologists, we proposed a 2.5D deep learning-based breast ultrasound tumor classification system. First, we used the pre-trained STU-Net to finetune and segment the tumor in 3D. Then, we fine-tuned the DenseNet-121 for classification using the 10 slices with the biggest tumoral area and their adjacent slices. The Tumor Detection, Segmentation, and Classification on Automated 3D Breast Ultrasound (TDSC-ABUS) MICCAI Challenge 2023 dataset was used to train and validate the performance of the proposed method. Compared to a 3D convolutional neural network model and radiomics, our proposed method has better performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhikai Yang, Tianyu Fan, Örjan Smedby, and Rodrigo Moreno "3D breast ultrasound image classification using 2.5D deep learning", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 131741R (29 May 2024); https://doi.org/10.1117/12.3025534

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