3D Breast Ultrasound Image Classification Using 2.5D Deep learning

QC 20240624Part of ISBN 978-151068020-3
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.

Fan, TianyuKTH,Medicinteknik och hälsosystem(Swepub:kth)u1b3r495 (author)
Smedby, Örjan,Professor,1956-KTH,Medicinsk avbildning(Swepub:kth)u1vc2uzb (author)
Moreno, Rodrigo,1973-KTH,Medicinsk avbildning(Swepub:kth)u1osc58y (author)
KTHMedicinsk avbildning (creator_code:org_t)

By the author/editor: Yang, Zhikai; Fan, Tianyu; Smedby, Örjan, P ...; Moreno, Rodrigo, ...

About the subject

MEDICAL AND HEALTH SCIENCES: MEDICAL AND HEAL ...; and Clinical Medicin ...; and Radiology Nuclea ...

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.