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3. CMG-Net: Robust Normal Estimation for Point Clouds via Chamfer Normal Distance and Multi-scale Geometry

Author

Wu, Yingrui, Zhao, Mingyang, Li, Keqiang, Quan, Weize, Yu, Tianqi, Yang, Jianfeng, Jia, Xiaohong, and Yan, Dong-Ming

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This work presents an accurate and robust method for estimating normals from point clouds. In contrast to predecessor approaches that minimize the deviations between the annotated and the predicted normals directly, leading to direction inconsistency, we first propose a new metric termed Chamfer Normal Distance to address this issue. This not only mitigates the challenge but also facilitates network training and substantially enhances the network robustness against noise. Subsequently, we devise an innovative architecture that encompasses Multi-scale Local Feature Aggregation and Hierarchical Geometric Information Fusion. This design empowers the network to capture intricate geometric details more effectively and alleviate the ambiguity in scale selection. Extensive experiments demonstrate that our method achieves the state-of-the-art performance on both synthetic and real-world datasets, particularly in scenarios contaminated by noise. Our implementation is available at https://github.com/YingruiWoo/CMG-Net_Pytorch., Comment: Accepted by AAAI 2024

Published
2023

8. Ship Image Segmentation Based on Multi-dimensional Information and Feature Fusion

Author

Cai, Yanxiang, Xie, Haicheng, Yang, Jianfeng, Yu, Tianqi, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Yadav, Sanjay, editor, Arya, Yogendra, editor, Pandey, Shailesh M., editor, Gherabi, Noredine, editor, and Karras, Dimitrios A., editor

Published
2024
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18. Synthesizing Evidence from the Earliest Studies to Support Decision-Making: To What Extent Could the Evidence Be Reliable?

Author

Yu, Tianqi, Lin, Lifeng, Furuya-Kanamori, Luis, and Xu, Chang

Abstract

In evidence-based practice, new topics generally only have a few studies available for synthesis. As a result, the evidence of such meta-analyses raised substantial concerns. We investigated the robustness of the evidence from these earliest studies. Real-world data from the Cochrane Database of Systematic Reviews (CDSR) were collected. We emulated meta-analyses with the earliest 1 to 10 studies through cumulative meta-analysis from eligible meta-analyses. The magnitude and the direction of meta-analyses with the earliest few studies were compared to the full meta-analyses. From the CDSR, we identified 20,227 meta-analyses of binary outcomes and 7683 meta-analyses of continuous outcomes. Under the tolerable difference of 20% on the magnitude of the effects, the convergence proportion ranged from 24.24% (earliest 1 study) to 77.45% (earliest 10 studies) for meta-analyses of few earliest studies with binary outcomes. For meta-analyses of continuous outcomes, the convergence proportion ranged from 13.86% to 56.52%. In terms of the direction of the effects, even when only three studies were available at the earliest stage, the majority had the same direction as full meta-analyses; Only 19% for binary outcomes and 12% for continuous outcomes changed the direction as further evidence accumulated. Synthesizing evidence from the earliest studies is feasible to support urgent decision-making, and in most cases, the decisions would be reasonable. Considering the potential uncertainties, it is essential to evaluate the confidence of the evidence of these meta-analyses and update the evidence when necessary.

Published
2022
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29. DeepACC:Automate Chromosome Classification based on Metaphase Images using Deep Learning Framework Fused with Prior Knowledge

Author

Luo, Chunlong, Yu, Tianqi, Luo, Yufan, Wang, Manqing, Yu, Fuhai, Li, Yinhao, Tian, Chan, Qiao, Jie, and Xiao, Li

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Chromosome classification is an important but difficult and tedious task in karyotyping. Previous methods only classify manually segmented single chromosome, which is far from clinical practice. In this work, we propose a detection based method, DeepACC, to locate and fine classify chromosomes simultaneously based on the whole metaphase image. We firstly introduce the Additive Angular Margin Loss to enhance the discriminative power of model. To alleviate batch effects, we transform decision boundary of each class case-by-case through a siamese network which make full use of prior knowledges that chromosomes usually appear in pairs. Furthermore, we take the clinically seven group criterion as a prior knowledge and design an additional Group Inner-Adjacency Loss to further reduce inter-class similarities. 3390 metaphase images from clinical laboratory are collected and labelled to evaluate the performance. Results show that the new design brings encouraging performance gains comparing to the state-of-the-art baselines., Comment: This work is supported by a fund from another hospital. Only Li Xiao conceived the idea and supervised Chunlong Luo to complete the work, the data provider did not participate in the research process. Thus, the authorships and institutional information are not correct. After careful consideration, I decide to withdraw this preprint version

Published
2020

35. DeepACEv2: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks

Author

Xiao, Li, Luo, Chunlong, Yu, Tianqi, Luo, Yufan, Wang, Manqing, Yu, Fuhai, Li, Yinhao, Tian, Chan, and Qiao, Jie

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Chromosome enumeration is an essential but tedious procedure in karyotyping analysis. To automate the enumeration process, we develop a chromosome enumeration framework, DeepACEv2, based on the region based object detection scheme. The framework is developed following three steps. Firstly, we take the classical ResNet-101 as the backbone and attach the Feature Pyramid Network (FPN) to the backbone. The FPN takes full advantage of the multiple level features, and we only output the level of feature map that most of the chromosomes are assigned to. Secondly, we enhance the region proposal network's ability by adding a newly proposed Hard Negative Anchors Sampling to extract unapparent but essential information about highly confusing partial chromosomes. Next, to alleviate serious occlusion problems, besides the traditional detection branch, we novelly introduce an isolated Template Module branch to extract unique embeddings of each proposal by utilizing the chromosome's geometric information. The embeddings are further incorporated into the No Maximum Suppression (NMS) procedure to improve the detection of overlapping chromosomes. Finally, we design a Truncated Normalized Repulsion Loss and add it to the loss function to avoid inaccurate localization caused by occlusion. In the newly collected 1375 metaphase images that came from a clinical laboratory, a series of ablation studies validate the effectiveness of each proposed module. Combining them, the proposed DeepACEv2 outperforms all the previous methods, yielding the Whole Correct Ratio(WCR)(%) with respect to images as 71.39, and the Average Error Ratio(AER)(%) with respect to chromosomes as about 1.17.

Published
2019

50. Green Syngas Production from Natural Lignin, Sunlight, and Water over Pt‐Decorated InGaN Nanowires.

Author

Yu, Tianqi, Li, Jingling, Li, Yixin, Qiu, Liang, Pan, Hu, Zhu, Lei, Huang, Zheng, and Zhou, Baowen

Subjects
*TURNOVER frequency (Catalysis), *INDIUM gallium nitride, *CHEMICAL bonds, *NANOWIRES, *SUNSHINE
Abstract

Transformation of lignin to syngas can turn waste into treasure yet remains a tremendous challenge because of its naturally evolved stubborn structure. In this work, light‐driven reforming of natural lignin in water for green syngas production is explored using Pt‐decorated InGaN nanowires. The spectroscopic characterizations, isotope, and model compound experiments, as well as density function theory calculation, disclose that among a variety of groups including aromatic ring, −OH, −OCH3, −C3H7 with complex chemical bonds of O−H, C−H, C−C, C−O, etc., InGaN nanowires are cooperative with Pt for preferably breaking the C−O bond of the rich O−CH3 group in lignin to liberating ⋅CH3 by photogenerated holes with a minimum dissociation energy of 2.33 eV. Syngas are subsequently yielded from the continuous evolution of ⋅CH3 and ⋅OH from photocatalytic reforming of lignin in water. Together with the superior optoelectronic attributes of Pt‐decorated InGaN nanowires, the evolution rate of syngas approaches 43.4 mol ⋅ g−1 ⋅ h−1 with tunable H2/CO ratios and a remarkable turnover number (TON) of 150, 543 mol syngas per mol Pt. Notably, the architecture demonstrates a high light efficiency of 12.1 % for syngas generation under focused light without any extra thermal input. Outdoor test ascertains the viability of producing syngas with the only inputs of natural lignin, water, and sunlight, thus presenting a low‐carbon route for synthesizing transportation fuels and value‐added chemicals. [ABSTRACT FROM AUTHOR]

Published
2024
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