Chengchuang Lin
Research Subjects
Chengchuang Lin has a total of 45 co-authors. Here are the top co-authors by number of shared publications:
- Aihua Yin
Medical genetic center, Guangdong Women and Children Hospital, Guangzhou, Guangdong, China
4 shared publications - Gansen Zhao
South China Normal University
4 shared publications - Hanbiao Chen
Medical Genetic Centre and Maternal and Children Metabolic-Genetic Key Laboratory, Guangdong Women and Children Hospital, Guangzhou 511400, China
4 shared publications Gansen Zhao
South China Normal University, Guangzhou, China
3 shared publications- Li Guo
Medical Genetic Centre and Maternal and Children Metabolic-Genetic Key Laboratory, Guangdong Women and Children Hospital, Guangzhou 511400, China
3 shared publications - Zhirong Yang
Department of Computer Science Norwegian University of Science and Technology
3 shared publications
Latest Publications
- Open Access
- 28 November 2025
- Published by Association for Computing Machinery (ACM)
- p. 196-204
Clustering-guided Generative Adversarial Network with Data Augmentation for Surface Defect Detection
- 28 November 2025
- Published by Association for Computing Machinery (ACM)
- p. 196-204
Open AccessClustering-guided Generative Adversarial Network with Data Augmentation for Surface Defect Detection
- Open Access
- 28 November 2025
- Published by Association for Computing Machinery (ACM)
- p. 73-78
Analysis and Evaluation of Baseline Models for Highway Traffic Incident Detection
- 28 November 2025
- Published by Association for Computing Machinery (ACM)
- p. 73-78
Open AccessAnalysis and Evaluation of Baseline Models for Highway Traffic Incident Detection
- Open Access
- 13 January 2025
- Published by MDPI AG in Sustainability
- Vol. 17 (2) , 551
Understanding Congestion Risk and Emissions of Various Travel Behavior Patterns Based on License Plate Recognition Data
Understanding vehicle travel behavior patterns is crucial for effectively managing urban traffic congestion and mitigating the associated risks and excessive emissions. Existing research predominantly focuses on commuting patterns, with limited attention given to the spatiotemporal characteristics of other travel behaviors, and sparse investigation into the congestion risks and emissions associated with these patterns. To address this gap, the present study examines various travel behavior patterns and their associated congestion risks and emissions, using one week of License Plate Recognition (LPR) data from the megacity expressway network. First, we classify vehicles into different travel modes based on spatiotemporal features extracted from the LPR data and propose a scalable mode recognition method suitable for large-scale applications. We then assess the congestion risks associated with each mode and estimate the excessive emissions resulting from congestion. The findings reveal notable differences in congestion risks among travel modes, with a bimodal distribution influenced by the temporal rhythm of traffic flow. Furthermore, although commercial vehicles constitute only one-third of the total vehicle population, the excessive emissions attributed to congestion from commercial vehicles are comparable to those from privately owned vehicles. This suggests that focusing exclusively on commuting patterns may underestimate both the congestion risks and excessive emissions. The results of this study not only deepen our understanding of the relationship between individual travel behavior and traffic congestion but also support the optimization of personal travel time and health management, providing a foundation for the development of personalized and proactive traffic demand management strategies.
- 13 January 2025
- Published by MDPI AG in Sustainability
- Vol. 17 (2) , 551
Open AccessUnderstanding Congestion Risk and Emissions of Various Travel Behavior Patterns Based on License Plate Recognition Data
Understanding vehicle travel behavior patterns is crucial for effectively managing urban traffic congestion and mitigating the associated risks and excessive emissions. Existing research predominantly focuses on commuting patterns, with limited attention given to the spatiotemporal characteristics of other travel behaviors, and sparse investigation into the congestion risks and emissions associated with these patterns. To address this gap, the present study examines various travel behavior patterns and their associated congestion risks and emissions, using one week of License Plate Recognition (LPR) data from the megacity expressway network. First, we classify vehicles into different travel modes based on spatiotemporal features extracted from the LPR data and propose a scalable mode recognition method suitable for large-scale applications. We then assess the congestion risks associated with each mode and estimate the excessive emissions resulting from congestion. The findings reveal notable differences in congestion risks among travel modes, with a bimodal distribution influenced by the temporal rhythm of traffic flow. Furthermore, although commercial vehicles constitute only one-third of the total vehicle population, the excessive emissions attributed to congestion from commercial vehicles are comparable to those from privately owned vehicles. This suggests that focusing exclusively on commuting patterns may underestimate both the congestion risks and excessive emissions. The results of this study not only deepen our understanding of the relationship between individual travel behavior and traffic congestion but also support the optimization of personal travel time and health management, providing a foundation for the development of personalized and proactive traffic demand management strategies.
- Open Access
- 5 December 2024
- Published by MDPI AG in Preprints.org
Understanding Congestion Risk and Emissions of Various Travel Behavior Patterns Based on License Plate Recognition Data
Understanding vehicle travel behavior patterns is essential for managing urban traffic congestion, as well as for addressing the associated congestion risks and excess emissions. This study, based on one week of License Plate Recognition (LPR) data from urban expressway networks, investigates different travel behavior patterns and their related congestion risks and emissions. First, we classify vehicles into distinct travel patterns based on spatiotemporal features extracted from LPR data and propose a scalable pattern recognition method suitable for large-scale applications. We then assess the congestion risks associated with each pattern and estimate the excess emissions resulting from congestion. The results reveal substantial variation in congestion risks across different travel patterns, with congestion risks following a bimodal distribution, influenced by temporal traffic flow rhythms. Furthermore, the excess emissions from congestion caused by commercially used vehicles (CUVs) are comparable to those of individually owned vehicles (IVs), despite CUVs constituting only one-third of the total vehicle count. This suggests that focusing solely on commuter travel modes underestimates both congestion risks and excess emissions.
- 5 December 2024
- Published by MDPI AG in Preprints.org
Open AccessUnderstanding Congestion Risk and Emissions of Various Travel Behavior Patterns Based on License Plate Recognition Data
Understanding vehicle travel behavior patterns is essential for managing urban traffic congestion, as well as for addressing the associated congestion risks and excess emissions. This study, based on one week of License Plate Recognition (LPR) data from urban expressway networks, investigates different travel behavior patterns and their related congestion risks and emissions. First, we classify vehicles into distinct travel patterns based on spatiotemporal features extracted from LPR data and propose a scalable pattern recognition method suitable for large-scale applications. We then assess the congestion risks associated with each pattern and estimate the excess emissions resulting from congestion. The results reveal substantial variation in congestion risks across different travel patterns, with congestion risks following a bimodal distribution, influenced by temporal traffic flow rhythms. Furthermore, the excess emissions from congestion caused by commercially used vehicles (CUVs) are comparable to those of individually owned vehicles (IVs), despite CUVs constituting only one-third of the total vehicle count. This suggests that focusing solely on commuter travel modes underestimates both congestion risks and excess emissions.
- 9 September 2023
- Published by Springer Nature
- p. 575-586
A Suitability Assessment Framework for Medical Cell Images in Chromosome Analysis
- 9 September 2023
- Published by Springer Nature
- p. 575-586
A Suitability Assessment Framework for Medical Cell Images in Chromosome Analysis
- 16 July 2022
- Published by Elsevier in Computational Biology and Chemistry
- Vol. 100, 107731
ChromosomeNet: A massive dataset enabling benchmarking and building basedlines of clinical chromosome classification
- 16 July 2022
- Published by Elsevier in Computational Biology and Chemistry
- Vol. 100, 107731
ChromosomeNet: A massive dataset enabling benchmarking and building basedlines of clinical chromosome classification
- 16 June 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Vol. 19 (1) , 31-39
A Clinical Dataset and Various Baselines for Chromosome Instance Segmentation
Background: In medicine, chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, chromosome instance segmentation is the most critical obstacle to automatic chromosome karyotyping analysis due to the complicated morphological characteristics of chromosome clusters, restricting chromosome karyotyping analysis to highly depend on skilled clinical analysts. Method: In this paper, we build a clinical dataset and propose multiple segmentation baselines to tackle the chromosome instance segmentation problem of various overlapping and touching chromosome clusters. First, we construct a clinical dataset for deep learning-based chromosome instance segmentation models by collecting and annotating 1,655 privacy-removal chromosome clusters. After that, we design a chromosome instance labeled dataset augmentation ( CILA ) algorithm for the clinical dataset to improve the generalization performance of deep learning-based models. Last, we propose a chromosome instance segmentation framework and implement multiple baselines for the proposed framework based on various instance segmentation models. Results and Conclusions: Experiments evaluated on the clinical dataset show that the best baseline of the proposed framework based on the Mask-RCNN model yields an outstanding result with$77\%$ $mAP$ ,$97.5\%$ $AP^{50}$ , and$95.5\%\ AP^{75}$ segmentation precision , and$95.38\%$ accuracy , which exceeds results reported in current chromosome instance segmentation methods. The quantitative evaluation results demonstrate the effectiveness and advancement of the proposed method for the chromosome instance segmentation problem. The experimental code and privacy-removal clinical dataset can be found at Github.- 16 June 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Vol. 19 (1) , 31-39
A Clinical Dataset and Various Baselines for Chromosome Instance Segmentation
Background: In medicine, chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, chromosome instance segmentation is the most critical obstacle to automatic chromosome karyotyping analysis due to the complicated morphological characteristics of chromosome clusters, restricting chromosome karyotyping analysis to highly depend on skilled clinical analysts. Method: In this paper, we build a clinical dataset and propose multiple segmentation baselines to tackle the chromosome instance segmentation problem of various overlapping and touching chromosome clusters. First, we construct a clinical dataset for deep learning-based chromosome instance segmentation models by collecting and annotating 1,655 privacy-removal chromosome clusters. After that, we design a chromosome instance labeled dataset augmentation ( CILA ) algorithm for the clinical dataset to improve the generalization performance of deep learning-based models. Last, we propose a chromosome instance segmentation framework and implement multiple baselines for the proposed framework based on various instance segmentation models. Results and Conclusions: Experiments evaluated on the clinical dataset show that the best baseline of the proposed framework based on the Mask-RCNN model yields an outstanding result with$77\%$ $mAP$ ,$97.5\%$ $AP^{50}$ , and$95.5\%\ AP^{75}$ segmentation precision , and$95.38\%$ accuracy , which exceeds results reported in current chromosome instance segmentation methods. The quantitative evaluation results demonstrate the effectiveness and advancement of the proposed method for the chromosome instance segmentation problem. The experimental code and privacy-removal clinical dataset can be found at Github.- 1 April 2021
- Published by Elsevier in Medical Image Analysis
- Vol. 69, 101943
A novel chromosome cluster types identification method using ResNeXt WSL model
- 1 April 2021
- Published by Elsevier in Medical Image Analysis
- Vol. 69, 101943
A novel chromosome cluster types identification method using ResNeXt WSL model
- 6 October 2020
- Published by Springer Nature
- p. 268-275
A Garbage Image Classification Framework Based on Deep Learning
- 6 October 2020
- Published by Springer Nature
- p. 268-275
A Garbage Image Classification Framework Based on Deep Learning
- 22 September 2020
- Published by Springer Nature
- p. 365-376
A Multi-Stages Chromosome Segmentation and Mixed Classification Method for Chromosome Automatic Karyotyping
- 22 September 2020
- Published by Springer Nature
- p. 365-376
A Multi-Stages Chromosome Segmentation and Mixed Classification Method for Chromosome Automatic Karyotyping