Publications

Quantitative Imaging


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Publications of D. Wang
Books and proceedings
  1. T. Liu, G. Webb, L. Yue, and D. Wang. AI 2023: Advances in Artificial Intelligence, 36th Australasian Joint Conference on Artificial Intelligence, volume 14472. Springer, December 2023. [WWW] [bibtex-entry]

  2. C. Sun, T. Bednarz, T. D. Pham, P. Vallotton, and D. Wang. Signal and Image Analysis for Biomedical and Life Sciences, volume 823 of Advances in Experimental Medicine and Biology. Springer, 2015. [WWW] [bibtex-entry]

  3. C. Sun, T. Bednarz, T. D. Pham, P. Vallotton, and D. Wang. Proceedings of International Symposium on Computational Models for Life Sciences, volume 1559. American Institute of Physics, 2013. [WWW] [bibtex-entry]

Articles in journal or book chapters
  1. D. Acharya, M. Farazi, V. Rolland, L. Petersson, U. Rosebrock, D. Smith, J. Ford, D. Wang, G. Tuck, R. Little, and C. Wilcox. Towards automatic anomaly detection in fisheries using electronic monitoring and automatic identification system. Fisheries Research, 272(106939), April 2024. [DOI] [bibtex-entry]

  2. D. Acharya, M. Saqib, C. Devine, C. Untiedt, R. Little, D. Wang, and G. Tuck. Using deep learning to automate the detection of bird scaring lines on fishing vessels. Biological Conservation, 296:1--9, August 2024. [DOI] [bibtex-entry]

  3. M. Alam, D. Wang, and A. Sowmya. AMFP-net: Adaptive multi-scale feature pyramid network for diagnosis of pneumoconiosis from chest X-ray images. Artificial Intelligence in Medicine, 154:1--11, August 2024. [DOI] [bibtex-entry]

  4. M. Alam, D. Wang, and A. Sowmya. DLA-Net: Dual Lesion Attention Network for classification of pneumoconiosis using chest X-Ray images. Scientific Reports, 14:1--12, May 2024. [DOI] [bibtex-entry]

  5. K. Lin, W. Wen, D. Lipnick, L. Mewton, R. Chen, J. Du, D. Wang, I. Skoog, R. Sterner, J. Najar, K. Kim, J. Han, J. Kim, T. Ng, R. Ho, D. Chua, K. Anstey, N. Cherbuin, M. Mortby, H. Brodaty, N. Kochan, P. Sachdev, and J. Jiang. Risk factors and cognitive correlates of white matter hyperintensities in ethnically diverse populations without dementia: the COSMIC consortium. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 16(1):1--11, March 2024. [DOI] [bibtex-entry]

  6. C. Liu, P. Li, L. Li, Z. Huang, D. Wang, and X. Yu. BAVS: Bootstrapping Audio-Visual Segmentation by Integrating Foundation Knowledge. IEEE Transactions on Multimedia, pp 1--13, May 2024. [DOI] [bibtex-entry]

  7. M. Saqib, M. R. Khokher, X. Yuan, B. Yan, D. Bearham, C. Devine, C. Untiedt, T. Cannard, K. Maguire, G. N. Tuck, R. Little, and D. Wang. Fishing Event Detection and Species Classification using Computer Vision and Artificial Intelligence for Electronic Monitoring. Fisheries Research, 280(107141):1-16, December 2024. [DOI] [bibtex-entry]

  8. Q. Zheng, D. Liu, C. Wang, J. Zhang, D. Wang, and D. Tao. ESceme: Vision-and-Language Navigation with Episodic Scene Memory. International Journal of Computer Vision, pp 1--21, July 2024. [DOI] [bibtex-entry]

  9. D. Ahmedt-Aristizabal, C. Nguyen, L. Tychsen-Smith, A. Stacey, S. Li, J. Pathikulangara, L. Petersson, and D. Wang. Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging. Computer Methods and Programs in Biomedicine, 232:107451, March 2023. [bibtex-entry]

  10. M. S. Alam, D. Wang, Q. Liao, and A. Sowmya. A Multi-scale Context aware Attention Model for Medical Image Segmentation. IEEE Journal of Biomedical and Health Informatics, pp 1--10, 2023. [DOI] [bibtex-entry]

  11. M. R. Khokher, Q. Liao, A. Smith, C. Sun, D. Mackenzie, M. R. Thomas, D. Wang, and E. Edwards. Early Yield Estimation in Viticulture based on Grapevine Inflorescence Detection and Counting in Videos. IEEE Access, 2023. [DOI] [bibtex-entry]

  12. H. Liu, D. Wang, K. Xu, P. Zhou, and D. Zhou. Lightweight convolutional neural network for counting densely piled steel bars. Automation in Construction, 146(104692):1--14, 2023. [DOI] [bibtex-entry]

  13. Q. Zheng, C. Wang, D. Wang, and D. Tao. Visual superordinate abstraction for robust concept learning. Machine Intelligence Research, 20(1):79--91, February 2023. [DOI] [bibtex-entry]

  14. L. Devnath, Z. Fan, S. Luo, P. Summons, and D. Wang. Detection and Visualisation of Pneumoconiosis Using an Ensemble of Multi-Dimensional Deep Features Learned from Chest X-rays. International Journal of Environmental Research and Public Health, 19(18):1-21, September 2022. [DOI] [bibtex-entry]

  15. L. Devnath, S. Luo, P. Summons, and D. Wang. Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker's Chest X-ray Radiography. Journal of Clinical Medicine, 11(18), September 2022. [DOI] [bibtex-entry]

  16. L. Devnath, P. Summons, S. Luo, D. Wang, K. Shaukat, I. Hameed, and H. Aljuaid. Computer-aided diagnosis of coal workers pneumoconiosis in chest X-ray radiographs using machine learning: A systematic review. International Journal of Environmental Research and Public Health, 19(4):1--23, May 2022. [DOI] [bibtex-entry]

  17. J. Jiang, D. Wang, Y. Song, P. Sachdev, and W. Wen. Computer-aided extraction of select MRI markers of cerebral small vessel disease: A systematic review. NeuroImage, 261(119528):1--18, August 2022. [DOI] [bibtex-entry]

  18. Y. Xu, X. Yu, J. Zhang, L. Zhu, and D. Wang. Weakly Supervised RGB-D Salient Object Detection With Prediction Consistency Training and Active Scribble Boosting. IEEE Transactions on Image Processing, 31:2148--2161, March 2022. [DOI] [bibtex-entry]

  19. S. Amirgholipour, W. Jia, L. Liu, X. Fan, D. Wang, and X. He. PDANet: Pyramid Density-aware Attention based Network for Accurate Crowd Counting. Neurocomputing, pp 1--16, April 2021. [DOI] [bibtex-entry]

  20. J. Ke, Y. Lu, Y. Shen, J. Deng, J. Wright, Y. Zhang, H. Qin, D. Wang, X. Liang, and F. Jiang. Quantitative Analysis of Abnormalities in Gynecologic Cytopathology with Deep Learning. Laboratory Investigation, 101:513--524, February 2021. [DOI] [bibtex-entry]

  21. M. R. Khokher, L. R. Little, G. N. Tuck, D. V. Smith, M. Qiao, C. Devine, H. O'Neill, J. Pogonoski, R. Arangio, and D. Wang. Early lessons in deploying cameras and artificial intelligence technology for fisheries catch monitoring: where machine learning meets commercial fishing. Canadian Journal of Fisheries and Aquatic Sciences, July 2021. [DOI] [bibtex-entry]

  22. Q. Liao, D. Wang, and M. Xu. Category Attention Transfer for Efficient Fine-Grained Visual Categorization. Pattern Recognition Letter, pp 1-7, November 2021. [DOI] [bibtex-entry]

  23. S. Lopez-Marcano, E. Jinks, C. J. Brown, D. Wang, B. Kusy, E. Ditria, and R. Connolly. Automatic detection of fish and tracking of movement for ecology. Ecology and Evolution, pp 1--10, May 2021. [DOI] [bibtex-entry]

  24. L. Mo, L. Zhu, J. Ma, D. Wang, and H. Wang. MDRSteg: large-capacity image steganography based on multi-scale dilated ResNet and combined chi-square distance loss. Journal of Electronic Imaging, 30(1):013018, February 2021. [DOI] [bibtex-entry]

  25. L. Zhu, X. Wen, L. Mo, J. Ma, and D. Wang. Robust location-secured high-definition image watermarking based on key-point detection and deep learning. Optik, 248, December 2021. [DOI] [bibtex-entry]

  26. X. Zong, Z. Chen, and D. Wang. Local-CycleGAN: A General End-to-End Network for Visual Enhancement. Applied Intelligence, 51:1947--1958, April 2021. [DOI] [bibtex-entry]

  27. M. Dai, G. Xiao, S. Cheng, D. Wang, and X. He. Structural Correlation Filters Combined with a Gaussian Particle Filter for Hierarchical Visual Tracking. Neurocomputing, February 2020. [DOI] [bibtex-entry]

  28. L. Devnath, S. Luo, P. Summons, and D. Wang. Automated Detection of Pneumoconiosis with Multilevel Deep Features Learned from Chest X-Ray Radiographs. Computers in Biology and Medicine, 2020. [bibtex-entry]

  29. M. Qiao, D. Wang, G. Tuck, L. Little, A. Punt, and M. Gerner. Deep learning methods applied to electronic monitoring data: Automated catch event detection for longline fishing. ICES Journal of Marine Science, 2020. [bibtex-entry]

  30. H. Sanicola, C. Stewart, M. Mueller, F. Ahmadic, D. Wang, S. Powell, K. Sarkar, K. Cutbush, M. Woodruff, and D. Brafman. Guidelines for Establishing a 3-D Printing Biofabrication Laboratory. Biotechnology Advances, 2020. [bibtex-entry]

  31. C. E. Stewart, C. Kan, B. R. Stewart, H. W. 3rd Sanicola, J. P. Jung, O. Sulaiman, and D. Wang. Machine intelligence for nerve conduit design and production. Journal of Biological Engineering, 14:25, 2020. [DOI] [bibtex-entry]

  32. D. He, K. Xu, and D. Wang. Design of multi-scale receptive field convolutional neural network for surface inspection of hot rolled steels. Image and Vision Computing, 89:12--20, September 2019. [bibtex-entry]

  33. M. Dai, S. Cheng, X. He, and D. Wang. Object tracking in the presence of shaking motions. Neural Computing and Applications, 2018. [bibtex-entry]

  34. L. Devnath, S. Luo, P. Summons, and D. Wang. Tuberculosis (TB) Classification in Chest Radiographs Using Deep Convolutional Neural Networks. International Journal of Advances in Science, Engineering, and Technology, 6(3), 2018. [bibtex-entry]

  35. Y. Liu, K. Xu, and D. Wang. Online Surface Defect Identification of Cold Rolled Strips Based on Local Binary Pattern and Extreme Learning Machine. Metals, 8(3):1--18, March 2018. [DOI] [bibtex-entry]

  36. S. Zhang, G. Tang, X. Liu, S. Luo, and D. Wang. Retinex based low-light image enhancement using guided filtering and variational framework. Optoelectronics Letters, 14(2):156--160, March 2018. [DOI] [bibtex-entry]

  37. P. Zhou, K. Xu, and D. Wang. Rail Profile Measurement Based on Line-structured Light Vision. IEEE Access, 2018. [DOI] [bibtex-entry]

  38. L. Zhu, M. Ma, Z. Zhang, P. Zhang, W. Wu, D. Wang, D. Zhang, X. Wang, and H. Wang. Hybrid deep learning for automated lepidopteran insect image classification. Oriental Insects, 51(2):79--91, 2017. [DOI] [bibtex-entry]

  39. R. Balez, N. Steiner, M. Engel, S. Sanz Munoz, J. Lum, Y. Wu, D. Wang, P. Vallotton, P. Sachdev, M. O'Connor, K. Sidhu, G. Münch, and L. Ooi. Neuroprotective effects of apigenin against inflammation, neuronal excitability and apoptosis in an induced pluripotent stem cell model of Alzheimer's disease. Scientific Reports, 6:31450, August 2016. [DOI] [bibtex-entry]

  40. X. Li, S. Luo, Q. Hu, J. Li, D. Wang, and F. Chiong. Automatic Lung Field Segmentation in X-ray Radiographs Using Statistical Shape and Appearance Models. Journal of Medical Imaging and Health Informatics, 6(2):338--348, April 2016. [DOI] [bibtex-entry]

  41. H. Tan, Y. Fu, D. Wang, X. Zhang, and T. Xiao. Quantitative analysis of 3D vasculature for evaluation of angiogenesis in liver fibrosis with SR-uCT. Nuclear Science and Techniques, 27(125):1--9, September 2016. [DOI] [bibtex-entry]

  42. H. Tan, D. Wang, R. Li, C. Sun, R. Lagerstrom, Y. He, Y. Xue, and T. Xiao. A robust method for high-precision quantification of the complex three-dimensional vasculatures acquired by X-ray microtomography. Journal of Synchrotron Radiation, 23(5):1216--1226, September 2016. [DOI] [bibtex-entry]

  43. D. Wang, L. Bischof, R. Lagerstrom, V. Hilsenstein, A. Hornabrook, and G. Hornabrook. Automated Opal Grading by Imaging and Statistical Learning. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(2):185--201, February 2016. [DOI] [bibtex-entry]

  44. F. Westling, C. Sun, D. Wang, and F. I. Alam. Fish Counting and Measurement: A Modular Framework and Implementation. In J. Zhou, X. Bai, and T. Caelli, editors,Computer Vision and Pattern Recognition in Environmental Informatics, chapter 3, pages 41--57. IGI Global, 2016. [DOI] [bibtex-entry]

  45. L. Zhu, X. Wang, D. Wang, and H. Wang. Single image depth estimation based on Convolutional Neural Network and sparse connected Conditional Random Field. Optical Engineering, 55(10):103101, October 2016. [DOI] [bibtex-entry]

  46. T. Bednarz, D. Wang, Y. Arzhaeva, R. Lagerstrom, P. Vallotton, N. Budett, A. Khassapov, P. Szul, S. Chen, C. Sun, L. Domanski, D. Thompson, T. Gureyev, and J. Taylor. Cloud Based Toolbox for Image Analysis, Processing and Reconstruction Tasks. In C. Sun, T. Bednarz, T. D. Pham, P. Vallotton, and D. Wang, editors,Signal and Image Analysis for Biomedical and Life Sciences, chapter 11, pages 191--205. Springer, 2015. [DOI] [bibtex-entry]

  47. R. Li, D. Wang, C. Sun, R. Lagerstrom, H. Tan, Y. He, and T. Xiao. Towards Automated Quantitative Vasculature Understanding via Ultra High-Resolution Imagery. In C. Sun, T. Bednarz, T. D. Pham, P. Vallotton, and D. Wang, editors,Signal and Image Analysis for Biomedical and Life Sciences, chapter 10, pages 177--189. Springer, 2015. [DOI] [bibtex-entry]

  48. H. Tan, D. Wang, Y. Xue, Y. Wang, Y. Yang, and T. Xiao. Implementation of Parallel Computation and Accelerating Analysis for 3D Thinning Skeletonisation Based on OpenMP. ACTA Optica Sinica, 35(11):1117003, 2015. [bibtex-entry]

  49. L. Zhu, Y. Zhou, D. Zhang, D. Wang, H. Wang, and X. Wang. Parallel multi-level 2D-DWT on CUDA GPUs and its application in ring artifact removal. Concurrency and Computation: Practice and Experience, 27(17):5188--5202, December 2015. [DOI] [bibtex-entry]

  50. M. Payne, D. Wang, C. Sinclair, R. Kapsa, A. Quigley, G. Wallace, J. Razal, R. Baughman, G. Münch, and P. Vallotton. Automated quantification of neurite outgrowth orientation distributions on patterned surfaces. Journal of Neural Engineering, 11(4):046006, 2014. [DOI] [bibtex-entry]

  51. D. Wang, D. Bourke, L. Domanski, and P. Vallotton. Speeding up the Analysis of Neuron Morphology using Parallel Processing. Journal of Molecular Imaging & Dynamics, 4:115, 2014. [DOI] [bibtex-entry]

  52. L. Zhu and D. Wang. Suppression of Ring artifacts in Synchrotron Radiation Images Using Heterogeneous Computing based on Improved Wavelet. Journal of Computational Information Systems, 10(2):523--530, 2014. [bibtex-entry]

  53. L. Zhu, D. Wang, and H. Wang. An improved method for the removal of ring artifacts in synchrotron radiation images by using GPGPU computing with compute unified device architecture. Concurrency and Computation: Practice and Experience, 26(18):2880--2892, December 2014. [DOI] [bibtex-entry]

  54. L. Domanski, T. Bednarz, T. Gureyev, L. Murray, B. E. Huang, Ya. I. Nesterets, D. Thompson, E. Jones, C. Cavanagh, D. Wang, P. Vallotton, C. Sun, A. Khassapov, A. Stevenson, S. Mayo, M. Morell, A. W. George, and J. A. Taylor. Applications of Heterogeneous Computing in Computational and Simulation Science. International Journal of Computational Science and Engineering, 8(3):240--252, 2013. [DOI] [bibtex-entry]

  55. L. Domanski, C. Sun, R. Lagerstrom, D. Wang, L. Bischof, M. Payne, and P. Vallotton. High Throughput Detection of Linear Features: Selected Applications in Biological Imaging. In G. Dougherty, editor, Medical Image Processing: Techniques and Applications, chapter 8, pages 167--191. Springer, 2011. [DOI] [bibtex-entry]

  56. D. Wang, R. Lagerstrom, C. Sun, L. Bischof, P. Vallotton, and M. Götte. HCA-Vision: Automated Neurite Outgrowth Analysis. Journal of Biomolecular Screening, 15(9):1165--1170, October 2010. [DOI] [bibtex-entry]

  57. M. Li, J. Xu, J. Yang, D. Yang, and D. Wang. Multiple Manifolds Analysis and Its Application to Fault Diagnosis. Mechanical Systems and Signal Processing, 23(8):2500--2509, November 2009. [bibtex-entry]

  58. C. Sun, P. Vallotton, D. Wang, J. Lopez, Y. Ng, and D. James. Membrane Boundary Extraction Using Circular Multiple Paths. Pattern Recognition, 42(4):523--530, April 2009. [DOI] [bibtex-entry]

  59. L. Zhang, J. Xu, J. Yang, D. Yang, and D. Wang. Multiscale morphology analysis and its application to fault diagnosis. Mechanical Systems and Signal Processing, 22(3):597--610, April 2008. [bibtex-entry]

  60. P. Vallotton, R. Lagerstrom, C. Sun, M. Buckley, D. Wang, M. De Silva, S.-S. Tan, and J. Gunnersen. Automated Analysis of Neurite Branching in Cultured Cortical Neurons Using HCA-Vision. Cytometry, Part A, 71A(10):889--895, October 2007. [DOI] [bibtex-entry]

Conference articles
  1. C. Liu, P. Li, Q. Yu, H. Sheng, D. Wang, L. Li, and X. Yu. Benchmarking Audio Visual Segmentation for Long-Untrimmed Videos. In CVPR, Seattle, USA, pages 22712--22722, 17-21 June 2024. [WWW] [bibtex-entry]

  2. H. Xu, X. Su, A. Sowmya, I. Katz, and D. Wang. SCD-NAS: Towards Zero-Cost Training in Melanoma Diagnosis. In ICME, Niagra Falls, Canada, 15-19 July 2024. [bibtex-entry]

  3. J. Zheng, Y. Yao, B. Han, D. Wang, and T. Liu. Enhancing Contrastive Learning For Ordinal Regression Via Ordinal Content Preserved Data Augmentation. In ICLR, Vienna, Austria, pages 1--17, 7-11 May 2024. [WWW] [bibtex-entry]

  4. Y. Bai, E. Yang, Z. Wang, Y. Du, B. Han, C. Deng, D. Wang, and T. Liu. Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping. In NeurIPS, New Orleans, USA, Dec. 10-16 2023. [bibtex-entry]

  5. H. Huang, H. Kang, S. Liu, O. Salvado, T. Rakotoarivelo, D. Wang, and T. Liu. PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels. In ICCV, Paris, France, 2-6 October 2023. IEEE. [bibtex-entry]

  6. C. Liu, P. Li, X. Qi, H. Zhang, L., D. Wang, and X. Yu. Audio-Visual Segmentation by Exploring Cross-Modal Mutual Semantics. In ACMMM, Ottawa, Canada, 28 November - 9 December 2023. Association for Computing Machinery. [bibtex-entry]

  7. H. Xu, D. Wang, I. Katz, and A. Sowmya. Detection of basal cell carcinoma in whole slide images. In MICCAI, Vancouver, Canada, 8-12 October 2023. [bibtex-entry]

  8. Y. Bai, E. Yang, Z. Wang, Y. Du, B. Han, C. Deng, D. Wang, and T. Liu. MSR: Making Self-supervised learning Robust to Aggressive Augmentations. In NeurIPS, New Orleans, USA, 28 Nov - 9 Dec 2022. [bibtex-entry]

  9. H. Xu, X. Su, S. You, T. Huang, F. Wang, C. Qian, C. Zhang, C. Xu, D. Wang, and A. Sowmya. Data Agnostic Filter Gating for Efficient Deep Networks. In ICASSP, Singapore, 22-27 May 2022. [bibtex-entry]

  10. H. Xu, D. Wang, and A. Sowmya. Multi-scale Alignment and Spatial ROI Module for COVID-19 Diagnosis. In Proceedings of IEEE World Congress on Computational Intelligence, Padova, Italy, 18-23 July 2022. IEEE. [bibtex-entry]

  11. M. S. Alam, A. Sowmya, and D. Wang. Image data augmentation for improving performance of deep learning-based model in pathological lung segmentation. In DICTA, Gold Coast, Queensland, Australia, 29 November - 1 December 2021. [bibtex-entry]

  12. M. S. Alam, A. Sowmya, and D. Wang. Bidirectional ConvLSTM for lung segmentation from chest X-ray images. In ICTAI, Washington, USA, 1-3 November 2021. [bibtex-entry]

  13. C. Nguyen, D. Wang, K. Richter, P. Valencia, and G. Bishop-Hurley. Video-based cattle identification and behaviour recognition. In DICTA, Gold Coast, Queensland, Australia, 29 November - 1 December 2021. [WWW] [bibtex-entry]

  14. L. Devnath, S. Luo, P. Summons, and D. Wang. Performance comparison of deep learning models for black lung detection on chest X-ray radiographs. In 3rd International Conference on Software Engineering and Information Management, Sydney, Australia, 2-15 January 2020. [bibtex-entry]

  15. D. Wang, Y. Arzhaeva, L. Devnath, M. Qiao, S. Amirgholipour, Q. Liao, R. McBean, J. Hillhouse, S. Luo, D. Meredith, K. Newbigin, and D. Yates. Automated Pneumoconiosis Detection on Chest X-Rays Using Cascaded Learning with Real and Synthetic Radiographs. In International Conference on Digital Image Computing: Techniques and Applications (DICTA), Melbourne, Australia, 29 November-2 December 2020. [bibtex-entry]

  16. L. Devnath, S. Luo, P. Summons, and D. Wang. An accurate black lung detection using transfer learning based on deep neural networks. In 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ), pages 1-6, 2-4 December 2019. [DOI] [bibtex-entry]

  17. Q. Liao, D. Wang, H. Holewa, and M. Xu. Squeezed Bilinear Pooling for Fine-Grained Visual Categorization. In International Conference on Computer Vision (ICCV) Workshop, Korea, 27 October - 2 November 2019. [bibtex-entry]

  18. S. Amirgholipour, X. He, W. Jia, D. Wang, and M. Zeibots. A-CCNN: Adaptive CCNN For Density Estimation and Crowd Counting. In 2018 IEEE International Conference on Image Processing, Athens, Greece, 7-10 October 2018. [bibtex-entry]

  19. Q. Liao, D. Wang, and M. Xu. Fine-Grained Categorization by Deep Part-Collaboration Convolution Net. In DICTA, Canberra, Australia, 10-13 December 2018. [bibtex-entry]

  20. F. Rusak, Y. Arzhaeva, and D. Wang. Content-Aware Image Augmentation for Medical Imaging Applications. In 20th International Conference on Radiology and Medical Imaging, London, United Kingdom, 27-28 September 2018. [bibtex-entry]

  21. D. Wang, G. Tuck, R. Little, and R. Li. Advances in the automated detection and recording of capture events from on-vessel video footage. In International Fisheries Observer and Monitoring Conference, Vigo, Spain, 11-15 June 2018. [bibtex-entry]

  22. Y. Zhao, D. Grillmeier, A. Pourkhesalian, C. Solnordal, D. Wang, C. Cprsi, L. Li, and L. Wardhaugh. Fundamental aspects of the rotating liquid sheet contactor. In 14th International Conference on Greenhouse Gas Control Technologies, Melbourne, Australia, 21-25 October 2018. [bibtex-entry]

  23. G. Zhou, G. Du, Y. Wang, D. Wang, and T. Xiao. A low dose and in-vivo imaging system based on equally sloped tomography. In IEEE International Symposium on Biomedical Imaging (ISBI), pages 60--63, 2017. IEEE. [DOI] [bibtex-entry]

  24. C. Sun, P. Flemons, Y. Gao, D. Wang, N. Fisher, and J. La Salle. Automated Image Analysis on Insect Soups. In Digital Image Computing: Techniques and Applications, Gold Coast, Queensland, Australia, pages 142--147, 30 November-2 December 2016. [DOI] [bibtex-entry]

  25. D. Wang, R. Lagerstrom, C. Sun, C. Laukamp, M. Quigley, L. Whitbourn, P. Mason, P. Connor, and L. Fisher. Automated Vein Detection for Drill Core Analysis by Fusion of Hyperspectral and Visible Image Data. In 23rd International Conference on Mechatronics and Machine Vision in Practice, Nanjing, China, 28-30 November 2016. [DOI] [bibtex-entry]

  26. D. Jenkins, M. Mahoney, R. Roest, H. Lomas, R. Pearce, R. Li, S. Mayo, and D. Wang. Micro-CT analysis of coke and its relationship to coke quality indicators. In 7th International Congress on the Science and Technology of Ironmaking, Cleveland Ohio, USA, 4-7 May 2015. [bibtex-entry]

  27. X. Li, S. Luo, Q. Hu, J. Li, and D. Wang. Rib Suppression in Chest Radiographs for Lung Nodule. In International Conference on Information and Automation, Lijing, China , pages 50--55, 8 - 10 August 2015. IEEE. [bibtex-entry]

  28. S. Luo, X. Li, D. Wang, J. Li, and C. Sun. Automatic Fish Recognition and Counting in the Video Footage of Fishery Operations. In International Conference on Computational Intelligence and Communication Networks, Jabalpur, India , pages 296--299, 12-14 December 2015. IEEE. [DOI] [bibtex-entry]

  29. S. Luo, X. Li, D. Wang, C. Sun, J. Li, and G. Tang. Intelligent Tuna Recognition for Fisheries Monitoring. In The 12th International Computer Conference on Wavelet Active Media Technology and Information Processing, Chengdu, China , pages 158--162, 18-20 December 2015. [DOI] [bibtex-entry]

  30. D. Wang, H. Tan, R. Li, Y. He, and T. Xiao. Automated Quantitative Analysis of Ultra High-Resolution 3D images of Vasculature and Microvasculature. In Symposium on Multi-scale and Multi-dimensional Synchrotron Radiation Imaging, Shanghai, China, 3-6 November 2015. [bibtex-entry]

  31. D. Jenkins, M. R. Mahoney, R. H. Pearce, A. D. Miller, S. Mayo, D. Wang, R. Roest, H. Lomas, and R. Li. Micro-CT analysis of the microstructure of metallurgical coke for evaluation of coke quality. In the 2nd International Congress on 3D Materials Science, Annecy, France, 29 June - 2 July 2014. [bibtex-entry]

  32. X. Tan, C. Sun, D. Wang, Y. Guo, and T. D. Pham. Soft Cost Aggregation with Multi-Resolution Fusion. In European Conference on Computer Vision, volume V, Zurich, Switzerland, pages 17--32, 6-12 September 2014. [DOI] [bibtex-entry]

  33. D. Wang, H. Tan, R. Li, Y. He, and T. Xiao. Automated Quantification of 3D Vasculature Using Synchrotron Radiation X-Ray Microtomography. In The 12th International Conference on X-Ray Microscopy, Melbourne, Australia, 26-31 October 2014. [bibtex-entry]

  34. F. Westling, C. Sun, and D. Wang. A modular learning approach for fish counting and measurement using stereo baited remote underwater video. In Digital Image Computing: Techniques and Applications, Wollongong, Australia, pages 263--269, 25-27 November 2014. [DOI] [bibtex-entry]

  35. T. Bednarz, P. Szul, Y. Arzhaeva, D. Wang, N. Burdett, A. Khassapov, S. Chen, P. Vallotton, R. Lagerstrom, T. Gureyev, and J. Taylor. Biomedical image analysis and processing in clouds. In 2013 International Symposium on Computational Models for Life Sciences, volume 1559, Sydney, Australia, pages 77--79, 27-29 November 2013. AIP. [bibtex-entry]

  36. S. Chen, T. Bednarz, P. Szul, D. Wang, Y. Arzhaeva, N. Burdett, A.hassapov, and J. Zic. Galaxy + Hadoop: Toward a Collaborative and Scalable Image Processing Toolbox in Cloud. In 11th International Conference on Service Oriented Computing, Berlin, Germany, pages 339-351, 2-5 December 2013. [bibtex-entry]

  37. C. Huang, L. Zhang, D. Wang, T. Wu, and Q. Tong. Decomposition of Volume Scattering, Polarized Light and Chlorophyll Fluorescence by In-Situ Polarization Measurement. In IEEE International Geoscience and Remote Sensing Symposium, Melbourne, Australia, 21-26 July 2013. [bibtex-entry]

  38. D. Wang, T. Bednarz, Y. Arzhaeva, P. Szul, S. Chen, N. Burdett, A. Khassapov, T. Gureyev, and J. Taylor. Cloud Computing for High Performance Image Analysis on a National Infrastructure. In 13th IEEE/ACM International Symposium On Cluster, Cloud And Grid Computing, Delft, Netherlands, pages 172--173, 13-16 May 2013. [bibtex-entry]

  39. D. Wang, T. Bednarz, Y. Arzhaeva, P. Szul, S. Chen, N. Burdett, A. Khassapov, T. Gureyev, and J. Taylor. Cloud-based Services for Biomedical Image Analysis. In The 3rd International Conference on Cloud Computing and Services Science, Aachen, Germany, pages 350--357, 8-10 May 2013. [bibtex-entry]

  40. T. Bednarz, D. Wang, Y. Arzhaeva, P. Szul, S. Chen, N. Burdett, A. Khassapov, T. Gureyev, and J. Taylor. Cloud-Based Image Analysis and Processing Toolbox for Biomedical Applications. In The 8th International Conference on eScience, Chicago, USA, 8-12 October 2012. [bibtex-entry]

  41. D. Wang. Automated Quantitative Analysis of 2D and 3D Biomedical Images. In 7th Medical Applications of Synchrotron Radiation Workshop, Shanghai, China, pages 15--16, 17-20 October 2012. [bibtex-entry]

  42. H. Yu and D. Wang. Comparison Study of Two Energy Minimization Based Image Segmentation Methods. In Digital Image Computing: Techniques and Applications, Noosa, Australia, pages 633--638, 6-8 December 2011. [DOI] [bibtex-entry]

  43. L. Domanski, C. Sun, R. Hassan, P. Vallotton, and D. Wang. Linear Feature Detection on GPUs. In Digital Image Computing: Techniques and Applications, Sydney, pages 649--656, 1-3 December 2010. [DOI] [bibtex-entry]

  44. R. Lagerstrom, L. Bischof, D. Wang, and V. Hilsenstein. Objective Image Based Grading of Opal Gemstones. In Proceedings of the 2010 International Conference on Image Processing, Computer Vision and Pattern Recognition, Las Vegas, USA, pages 125--129, 12-15 July 2010. CSREA Press. [bibtex-entry]

  45. D. Wang, L. Bischof, B. Dai, R. Lagerstrom, G. Hornabrook, and P. Sutton. Accelerated Gemological Image Analysis for Opal Grading. In Proceedings of The 31st International Congress on Imaging Sciences, Beijing, pages 76--79, 12-16 May 2010. [bibtex-entry]

  46. D. Wang and P. Vallotton. Improved Marker-Controlled Watershed Segmentation with Local Boundary Priors. In Proceedings of Image and Vision Computing New Zealand, Queenstown, New Zealand, 8-9 November 2010. [bibtex-entry]

  47. D. Wang, K. Xu, and J. Xu. Morphological Image Analysis for Automated Surface Quality Inspection of Moving Steel Plates. In Proceedings of The 31st International Congress on Imaging Sciences, Beijing, pages 42--45, 12-16 May 2010. [bibtex-entry]

  48. L. Domanski, P. Vallotton, and D. Wang. Two and Three-Dimensional Image Deconvolution on Graphics Hardware. In Proceedings of the 18th World IMACS/MODSIM Congress, Cairns, Australia, pages 1010--1016, 13-17 July 2009. [WWW] [bibtex-entry]

  49. P. Vallotton, C. Sun, D. Wang, P. Ranganathan, L. Turnbull, and C. Whitchurch. Segmentation and tracking of individual Pseudomonas aeruginosa bacteria in dense populations of motile cells. In Image and Vision Computing New Zealand, Wellington, New Zealand, pages 221--225, 23-25 November 2009. [bibtex-entry]

  50. D. Wang, D. Bourke, L. Domanski, and P. Vallotton. Multicore-based high performance image analysis for batch processing in drug discovery. In Proceedings of the 18th World IMACS/MODSIM Congress, Cairns, pages 1080--1086, 13-17 July 2009. [bibtex-entry]

  51. C. Sun, P. Vallotton, D. Wang, Jamie Lopez, Yvonne Ng, and David James. Membrane Boundary Extraction Using a Circular Shortest Path Technique. In Tuan D. Pham and Xiaobo Zhou, editors, in Proceedings of International Symposium on Computational Models for Life Sciences, Gold Coast, Queensland, Australia, pages 41--47, 17-19 December 2007. [bibtex-entry]

Miscellaneous
  1. P. Vallotton, C. Sun, and D. Wang. HCA-Vision puts neuron images under a virtual scalpel. Innovation, 7(1):38--39, 2007. [WWW] [bibtex-entry]

  2. L. Bischof, M.J. Buckley, R. Lagerstrom, C. Sun, H. Talbot, D. Wang, and P. Vallotton. Image Analysis of Neurite Branching: High-Content Screening at High Speed. American Biotechnology Laboratory, 23(10):22, September 2005. [WWW] [bibtex-entry]


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