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20182020
most citedAccurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST

12 citations · 34 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.CV2020

Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale

Jinzheng Cai, Adam P. Harrison, Youjing Zheng +5

Acquiring large-scale medical image data, necessary for training machine learning algorithms, is frequently intractable, due to prohibitive expert-driven annotation costs. Recent d…

cs.CV201912 cited

End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation

Jinzheng Cai, Yingda Xia, Dong Yang +3

Automatic segmentation of abdomen organs using medical imaging has many potential applications in clinical workflows. Recently, the state-of-the-art performance for organ segmentat…

cs.CV201910 cited

Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition

Zizhao Zhang, Adriana Romero, Matthew J. Muckley +3

The goal of MRI reconstruction is to restore a high fidelity image from partially observed measurements. This partial view naturally induces reconstruction uncertainty that can onl…

cs.CV2018

Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation

Xiaowei Xu, Qing Lu, Yu Hu +4

With pervasive applications of medical imaging in health-care, biomedical image segmentation plays a central role in quantitative analysis, clinical diagno- sis, and medical interv…

cs.CV201812 cited

Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST

Jinzheng Cai, Youbao Tang, Le Lu +5

Volumetric lesion segmentation via medical imaging is a powerful means to precisely assess multiple time-point lesion/tumor changes. Because manual 3D segmentation is prohibitively…