Sen Liang
Jilin University
12 Papers
28 Citations
Sen Liang is an academic researcher from Jilin University. The author has contributed to research in topics: Computer science & Image noise. The author has an hindex of 5, co-authored 11 publications. Previous affiliations of Sen Liang include University of Georgia.
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Papers
•Proceedings Article
AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis
Yudong Guo,Keyu Chen,Sen Liang,Yong-Jin Liu,Hujun Bao,Juyong Zhang +5 more
- 20 Mar 2021
TL;DR: In this paper, the feature of input audio signal is directly fed into a conditional implicit function to generate a dynamic neural radiance field, from which a high-fidelity talking-head video corresponding to the audio signal was synthesized using volume rendering.
Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas
TL;DR: A novel multimodal 3D DenseNet model, with the properties of automatic feature extraction, and effective and high generalizability, M3D-DenseNet can serve as a useful method for other multi-modality radiogenomics problems and has the potential to be applied in clinical decision making.
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A Review of Matched-pairs Feature Selection Methods for Gene Expression Data Analysis
TL;DR: This review aims to induce and comprehensively present the matched-pairs feature selection methods in such a way that readers can easily understand its characteristics and get a clue in selecting the appropriate methods for their analyses.
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Automatic Cardiothoracic Ratio Calculation With Deep Learning
TL;DR: Performance of this computer-aided technique was demonstrated to be more reliable, time and labor saving than that of the manual method in CTR calculation, and diagnostic accuracy, specificity, and positive predictive value were comparable between the two methods.
•Posted Content
Cross-view Relation Networks for Mammogram Mass Detection.
TL;DR: A novel mass detection framework to capture the latent relation information from the two paired views of a same mass in mammogram is proposed, demonstrating that the proposed method outperforms existing feature fusion approaches and state-of-the-art mass detection methods.
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