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: astronomical imagery and data; camera calibration; 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. PDF 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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  2. PDF 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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  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. Khanal S, Chen J, Jacobs N, Lin A-L. 2021. Alzheimer’s Disease Classification Using Genetic Data. In: Machine Learning and Artificial Intelligence in Bioinformatics and Medical Informatics (MABM).
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  5. PDF 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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  6. 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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  7. 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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  8. PDF 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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  9. PDF 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. PDF 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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  11. PDF 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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  12. PDF 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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  13. PDF 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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  14. 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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  15. PDF 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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  16. PDF 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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  17. 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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  18. 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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  19. PDF 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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  20. 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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  21. 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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  22. 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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  23. 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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  24. 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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  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. 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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  28. 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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  29. PDF 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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  30. PDF 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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