Code for our NeurIPS'19 paper "Learning Deep Bilinear Transformation for Fine-grained Image Representation" - researchmm/DBTNet
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image reconstructiontomosynthesis imagingPurpose The goal of this study is to develop a three-dimensional (3D) iterative reconstruction framework based on the deep learning (DL) technique to improve the digital breast tomosynthesis (DBT) imaging performance.Su, TingDeng, XiaoleiYang, JiechengWang, Zhen...
内容提示: arXiv:2008.08322v1 [physics.med-ph] 19 Aug 2020DIR-DBTnet: Deep iterative reconstruction network for 3D digital breasttomosynthesis imagingTing Su, 1, a) Xiaolei Deng, 1, a) Zhenwei Wang, 2 Jiecheng Yang, 1 Jianwei Chen, 1 HairongZheng, 3 Dong Liang, 1,3, b) and Yong...
To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture. Fir...
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So to reduce the blast radius of potential bugs in the dbt implementation, in we are recommending using the new v1.9 snapshot configurations with net new snapshots rather than pre-existing ones. This is in the same spirit asdbt-labs/dbt-core#10923(which might not be possible or reasonable ...
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