SCSGNet: Spatial-Correlated and Shape-Guided Network for Breast Mass Segmentation
Author:
Affiliation:
1. Fudan University,School of Academy for Engineering and Technology,Shanghai,China
2. Fudan University,School of Computer Science, Shanghai Key Lab of Intelligent Information Processing,Shanghai,China
Funder
National Natural Science Foundation of China
Science and Technology Commission of Shanghai Municipality
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10096410.pdf?arnumber=10096410
Reference23 articles.
1. INbreast
2. Cascaded Partial Decoder for Fast and Accurate Salient Object Detection
3. UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
4. Receptive Field Block Net for Accurate and Fast Object Detection
5. A curated mammography data set for use in computer-aided detection and diagnosis research;lee;Scientific Data,2017
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1. AWDS-net: automatic whole-field segmentation network for characterising diverse breast masses;Connection Science;2024-01-08
2. Mammo-SAM: Adapting Foundation Segment Anything Model for Automatic Breast Mass Segmentation in Whole Mammograms;Machine Learning in Medical Imaging;2023-10-15
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