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3D Breast Ultrasoun...
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Yang, ZhikaiKTH,Medicinsk avbildning
(författare)
3D Breast Ultrasound Image Classification Using 2.5D Deep learning
- Artikel/kapitelEngelska2024
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SPIE,2024
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LIBRIS-ID:oai:DiVA.org:kth-348289
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https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-348289URI
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https://doi.org/10.1117/12.3025534DOI
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Språk:engelska
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Sammanfattning på:engelska
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QC 20240624Part of ISBN 978-151068020-3
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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.
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Smedby, Örjan,Professor,1956-KTH,Medicinsk avbildning(Swepub:kth)u1vc2uzb
(författare)
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Moreno, Rodrigo,1973-KTH,Medicinsk avbildning(Swepub:kth)u1osc58y
(författare)
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KTHMedicinsk avbildning
(creator_code:org_t)
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Ingår i:17th International Workshop on Breast Imaging, IWBI 2024: SPIE
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