Botong Wu
Peking University
18 Papers
38 Citations
Botong Wu is an academic researcher from Peking University. The author has contributed to research in topics: Computer science & Spurious correlation. The author has an hindex of 8, co-authored 18 publications. Previous affiliations of Botong Wu include Tsinghua University & Sun Yat-sen University.
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Papers
•Proceedings Article
Quantized correlation hashing for fast cross-modal search
Botong Wu,Qiang Yang,Wei-Shi Zheng,Yizhou Wang,Jingdong Wang +4 more
- 25 Jul 2015
TL;DR: This work presents a cross-modal hashing approach, called quantized correlation hashing (QCH), which takes into consideration the quantization loss over domains and the relation between domains, and outperforms the state-of-the-art multi- modal hashing methods.
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Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning–assisted nodule segmentation
Linlin Qi,Botong Wu,Wei Tang,Lina Zhou,Yao Huang,Shijun Zhao,Li Liu,Meng Li,Li Zhang,Shi-Chao Feng,Donghui Hou,Zhen Zhou,Xiuli Li,Yizhou Wang,Ning Wu,Jianwei Wang +15 more
TL;DR: In this article, the authors investigated the natural history of persistent pulmonary pure ground-glass nodules (pGGNs) with deep learning-assisted nodule segmentation, and they used a convolutional neural network (CNN) to segment pGGNs.
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Learning With Unsure Data for Medical Image Diagnosis
Botong Wu,Xinwei Sun,Lingjing Hu,Yizhou Wang +3 more
- 27 Oct 2019
TL;DR: A unified end-to-end learning framework is proposed, which also considers the aforementioned two issues: (i) incorporate cost-sensitive parameters to alleviate the data imbalance problem, and (ii) execute the conservative and aggressive strategies by introducing two parameters in the training procedure.
Causal Hidden Markov Model for Time Series Disease Forecasting
Jing Li,Botong Wu,Xinwei Sun,Yizhou Wang +3 more
- 30 Mar 2021
TL;DR: Zhang et al. as mentioned in this paper proposed a causal hidden Markov model to achieve robust prediction of irreversible disease at an early stage, which is safety-critical and vital for medical treatment in early stages.
Zero-Shot Learning posed as a Missing Data Problem
TL;DR: Zhang et al. as mentioned in this paper pose zero-shot learning as the missing data problem, rather than the missing label problem, and propose a transductive framework to estimate data distribution of unseen classes in the image feature space by transferring knowledge from the label embedding space.
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