Bibliographic Details
| Title: |
Automatic Breast Lesion Classification by Joint Neural Analysis of Mammography and Ultrasound |
| Authors: |
Habib, Gavriel, Kiryati, Nahum, Sklair-Levy, Miri, Shalmon, Anat, Neiman, Osnat Halshtok, Weidenfeld, Renata Faermann, Yagil, Yael, Konen, Eli, Mayer, Arnaldo |
| Publication Year: |
2020 |
| Collection: |
Computer Science |
| Subject Terms: |
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition |
| More Details: |
Mammography and ultrasound are extensively used by radiologists as complementary modalities to achieve better performance in breast cancer diagnosis. However, existing computer-aided diagnosis (CAD) systems for the breast are generally based on a single modality. In this work, we propose a deep-learning based method for classifying breast cancer lesions from their respective mammography and ultrasound images. We present various approaches and show a consistent improvement in performance when utilizing both modalities. The proposed approach is based on a GoogleNet architecture, fine-tuned for our data in two training steps. First, a distinct neural network is trained separately for each modality, generating high-level features. Then, the aggregated features originating from each modality are used to train a multimodal network to provide the final classification. In quantitative experiments, the proposed approach achieves an AUC of 0.94, outperforming state-of-the-art models trained over a single modality. Moreover, it performs similarly to an average radiologist, surpassing two out of four radiologists participating in a reader study. The promising results suggest that the proposed method may become a valuable decision support tool for breast radiologists.
Comment: 10 pages including references, 8 figures, 1 table. Accepted to MICCAI ML-CDS 2020 workshop (ML-CDS 2020/CLIP 2020, LNCS 12445 proceedings) |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2009.11009 |
| Accession Number: |
edsarx.2009.11009 |
| Database: |
arXiv |
