Multimodal Vision Research Laboratory

MVRL

research area: medical and biological imaging

See below for a list of our publications in this area. You can see an unfiltered list of our publications or lists filtered for the following research areas: agriculture; astronomical imagery and data; camera calibration; biodiversity and conservation; generative modeling; LiDAR Processing; image localization; medical and biological imaging; image motion; remote sensing and mapping; social media; video surveillance and object tracking; timelapse imaging; transportation; and outdoor webcam imagery.

publications

  1. Xing X, Liang G, Wang C, Jacobs N, Lin A-L. 2023. Self-Supervised Learning Application on COVID-19 Chest X-ray Image Classification Using Masked AutoEncoder. Bioengineering 10. DOI: 10.3390/bioengineering10080901.
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  2. Xing X, Rafique MU, Liang G, Blanton H, Zhang Y, Wang C, Jacobs N, Lin A-L. 2023. Efficient Training on Alzheimer’s Disease Diagnosis with Learnable Weighted Pooling for 3D PET Brain Image Classification. Electronics 12. DOI: 10.3390/electronics12020467.
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  3. Xing X, Liang G, Zhang Y, Khanal S, Lin A-L, Jacobs N. 2022. ADViT: VISION TRANSFORMER ON MULTI-MODALITY PET IMAGES FOR ALZHEIMER DISEASE DIAGNOSIS. In: IEEE International Symposium on Biomedical Imaging (ISBI). DOI: 10.1109/ISBI52829.2022.9761584.
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  4. Liang G, Ganesh H, Steffe D, Liu L, Jacobs N, Zhang J. 2022. Development of CNN models for the enteral feeding tube positioning assessment on a small scale data set. BMC Medical Imaging 22. DOI: 10.1186/s12880-022-00766-w.
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  5. Khanal S, Chen J, Jacobs N, Lin A-L. 2021. Alzheimer’s Disease Classification Using Genetic Data. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
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  6. Ying Q, Xing X, Liu L, Lin A-L, Jacobs N, Liang G. 2021. Multi-Modal Data Analysis for Alzheimer’s Disease Diagnosis: An Ensemble Model Using Imagery and Genetic Features. In: International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). DOI: 10.1109/EMBC46164.2021.9630174.
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  7. Liang G, Xing X, Liu L, Zhang Y, Ying Q, Lin A-L, Jacobs N. 2021. Alzheimer’s Disease Classification Using 2D Convolutional Neural Networks. In: International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). DOI: 10.1109/EMBC46164.2021.9629587.
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  8. a thumbnail for Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging
    Liang G, Greenwell C, Zhang Y, Xing X, Wang X, Kavuluru R, Jacobs N. 2021. Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging. IEEE Journal of Biomedical and Health Informatics 26. DOI: 10.1109/JBHI.2021.3110805.
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  9. a thumbnail for Improved Trainable Calibration Method for Neural Networks
    Liang G, Zhang Y, Wang X, Jacobs N. 2020. Improved Trainable Calibration Method for Neural Networks. In: British Machine Vision Conference (BMVC).
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  10. Hammond TC, Xing X, Wang C, Ma D, Nho K, Crane PK, Elahi F, Ziegler DA, Liang G, Cheng Q, Yanckello LM, Jacobs N, Lin A-L. 2020. Beta-amyloid and tau drive early Alzheimer’s disease decline while glucose hypometabolism drives late decline. Communications Biology 3:352. DOI: 10.1038/s42003-020-1079-x.
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  11. Wang X, Liang G, Zhang Y, Blanton H, Bessinger Z, Jacobs N. 2020. Inconsistent Performance of Deep Learning Models on Mammogram Classification. Journal of the American College of Radiology. DOI: 10.1016/j.jacr.2020.01.006.
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  12. Xing X, Liang G, Blanton H, Rafique MU, Wang C, Lin A-L, Jacobs N. 2020. Dynamic Image for 3D MRI Image Alzheimer’s Disease Classification. In: ECCV Workshop on BioImage Computing (BIC).
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  13. Liang G, Zhang Y, Jacobs N. 2020. Neural Network Calibration for Medical Imaging Classification Using DCA Regularization. In: ICML 2020 workshop on Uncertainty and Robustness in Deep Learning (UDL).
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  14. Liang G, Wang X, Zhang Y, Jacobs N. 2020. Weakly-Supervised Self-Training for Breast Cancer Localization. In: International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). DOI: 10.1109/EMBC44109.2020.9176617.
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  15. Mihail RP, Liang G, Jacobs N. 2019. Automatic Hand Skeletal Shape Estimation from Radiographs. IEEE Transactions on NanoBioscience 18:296–305. DOI: 10.1109/TNB.2019.2911026.
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  16. Zhang Y, Wang X, Blanton H, Liang G, Xing X, Jacobs N. 2019. 2D Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM). DOI: 10.1109/BIBM47256.2019.8983097.
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  17. Hammond T, Xing X, Jacobs N, Lin A-L. 2019. Phase-dependent importance of amyloid-beta, phosphorylated-tau, and hypometabolism in determining mild cognitive impairment and Alzheimer’s disease: A machine learning study. In: Alzheimer’s Disease Therapeutics: Alternatives to Amyloid.
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  18. Liang G, Wang X, Zhang Y, Xing X, Blanton H, Salem T, Jacobs N. 2019. Joint 2D-3D Breast Cancer Classification. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM). DOI: 10.1109/BIBM47256.2019.8983048.
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  19. Zhang Y, Liang G, Jacobs N, Wang X. 2019. Unsupervised Domain Adaptation for Mammogram Image Classification: A Promising Tool for Model Generalization. In: Conference on Machine Intelligence in Medical Imaging (CMIMI).
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  20. Liang G, Jacobs N, Wang X. 2019. Training Deep Learning Models as Radiologists: Breast Cancer Classification Using Combined whole 2D Mammography and full volume Digital Breast Tomosynthesis. In: Radiological Society of North America (RSNA).
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  21. a thumbnail for GANai: Standardizing CT Images using Generative Adversarial Network with Alternative Improvement
    Liang G, Fouladvand S, Zhang J, Brooks MA, Jacobs N, Chen J. 2019. GANai: Standardizing CT Images using Generative Adversarial Network with Alternative Improvement. In: IEEE International Conference on Healthcare Informatics (ICHI). DOI: 10.1109/ICHI.2019.8904763.
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  22. Liang G, Jacobs N, Liu J, Luo K, Owen W, Wang X. 2019. Translational relevance of performance of deep learning models on mammograms. In: SBI/ACR Breast Imaging Symposium.
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  23. Zhang X, Zhang Y, Han E, Jacobs N, Han Q, Wang X, Liu J. 2018. Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE Transactions on NanoBioscience. DOI: 10.1109/TNB.2018.2845103.
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  24. a thumbnail for Automatic Hand Skeletal Shape Estimation from Radiographs
    Mihail RP, Jacobs N. 2018. Automatic Hand Skeletal Shape Estimation from Radiographs. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM). DOI: 10.1109/BIBM.2018.8621196.
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  25. Jones D, Jacobs N, Ellingson S. 2018. Learning Deep Feature Representations for Kinase Polypharmacology. In: ACM Richard Tapia Celebration of Diversity in Computing Conference.
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  26. Jones D, Bopaiah J, Alghamedy F, Jacobs N, Weiss H, Jong WAD, Ellingson S. 2018. Polypharmacology Within the Full Kinome: a Machine Learning Approach. In: AMIA Informatics Summit.
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  27. Liang G, Wang X, Jacobs N. 2018. Evaluating the Publicly Available Mammography Datasets for Deep Learning Model Training. In: SBI/ACR Breast Imaging Symposium.
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  28. Zhang X, Zhang Y, Han E, Jacobs N, Han Q, Wang X, Liu J. 2017. Whole Mammogram Image Classification With Convolutional Neural Networks. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM). DOI: 10.1109/BIBM.2017.8217738.
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  29. Mihail RP, Jacobs N, Goldsmith J, Lohr K. 2015. Using Visual Analytics to Inform Rheumatoid Arthritis Patient Choices. In: Loh CS, Sheng Y, Ifenthaler D eds. Serious Games Analytics. Advances in Game-Based Learning. Springer International Publishing, 211–231. DOI: 10.1007/978-3-319-05834-4_9.
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  30. Mihail RP, Blomquist G, Jacobs N. 2014. A CRF Approach to Fitting a Generalized Hand Skeleton Model. In: IEEE Winter Conference on Applications of Computer Vision (WACV). 409–416. DOI: 10.1109/WACV.2014.6836070.
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  31. Dixon M, Jacobs N, Pless R. 2006. Finding Minimal Parameterizations of Cylindrical Image Manifolds. In: IEEE CVPR Workshop on Perceptual Organization in Computer Vision (POCV). 1–8. DOI: 10.1109/CVPRW.2006.82.
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