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Xiaohui Xie
Researcher at University of California, Irvine
Publications - 351
Citations - 34195
Xiaohui Xie is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 58, co-authored 220 publications receiving 29844 citations. Previous affiliations of Xiaohui Xie include University of California, Berkeley & National Chiao Tung University.
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AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation.
TL;DR: In this article, Axial Fusion Transformer UNet (AFTer-UNet) is proposed, which takes both advantages of convolutional layers' capability of extracting detailed features and transformers' strength on long sequence modeling.
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AnatomyNet: Deep 3D Squeeze-and-excitation U-Nets for fast and fully automated whole-volume anatomical segmentation
TL;DR: This work proposes an end-to-end, fast and fully automated deep convolutional network, AnatomyNet, for accurate and whole-volume HaN anatomical segmentation, which outperforms previous state-of-the-art methods on the benchmark dataset.
Journal ArticleDOI
Interactive and fuzzy search
TL;DR: A new system for exploring the entire MEDLINE collection is proposed, represented by two unique features: interactive: providing instant feedback to users' query letter by letter, and fuzzy: allowing approximate search.
Journal ArticleDOI
Lineage tracing and analog recording in mammalian cells by single-site DNA writing.
Theresa B Loveless,Joseph H. Grotts,Mason W. Schechter,Elmira Forouzmand,Courtney K. Carlson,Bijan S. Agahi,Guohao Liang,Michelle Ficht,Beide Liu,Xiaohui Xie,Chang C. Liu +10 more
TL;DR: Chyron as discussed by the authors is a DNA recorder that acts primarily by writing new DNA through the repeated insertion of random nucleotides at a single locus in temporal order, and combines Cas9, a homing guide RNA and the template-independent DNA polymerase terminal deoxynucleotidyl transferase.
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Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
TL;DR: In this paper, the authors proposed an end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs, which showed the robustness of proposed networks compared to previous work using segmentation and detection annotations.