2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-BSIP.10
Paper Title LIVER TUMOR DETECTION VIA A MULTI-SCALE INTERMEDIATE MULTI-MODAL FUSION NETWORK ON MRI IMAGES
Authors Chao Pan, Tongji University, China; Peiyun Zhou, Zhongshan Hospital, Fudan University, China; Jingru Tan, Tongji University, China; Baoye Sun, Ruoyu Guan, Zhutao Wang, Zhongshan Hospital, Fudan University, China; Ye Luo, Jianwei Lu, Tongji University, China
SessionMLR-APPL-BSIP: Machine learning for biomedical signal and image processing
LocationArea C
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for biomedical signal and image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Automatic liver tumor detection can assist doctors to make effective treatments. However, how to utilize multi-modal images to improve detection performance is still challenging. Common solutions for using multi-modal images consist of early, inter-layer, and late fusion. They either do not fully consider the intermediate multi-modal feature interaction or have not put their focus on tumor detection. In this paper, we propose a novel multi-scale intermediate multi-modal fusion detection framework to achieve multi-modal liver tumor detection. Unlike early or late fusion, it maintains two branches of different modal information and introduces cross-modal feature interaction progressively, thus better leveraging the complementary information contained in multi-modalities. To further enhance the multi-modal context at all scales, we design a multi-modal enhanced feature pyramid. Extensive experiments on the collected liver tumor magnetic resonance imaging (MRI) dataset show that our framework outperforms other state-of-the-art detection approaches in the case of using multi-modal images.