Big Data Category
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
Aug. 31, 2023—Xin Yu, Qi Yang, Yinchi Zhou, Leon Y. Cai , Riqiang Gao, Ho Hin Lee, Thomas Li, Shunxing Bao, Zhoubing Xu, Thomas A. Lasko, Richard G. Abramson, Zizhao Zhang, Yuankai Huo, Bennett A. Landman, Yucheng Tang Paper: https://arxiv.org/abs/2209.14378 Code: https://github.com/Project-MONAI/model-zoo/tree/dev/models Abstract Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional repre- sentation learning capabilities...
pyPheWAS Explorer: a visualization tool for exploratory analysis of phenome-disease associations
Aug. 31, 2023—Cailey I. Kerley, Karthik Ramadass, Tin Q. Nguyen, Laurie E. Cutting, Bennett A. Landman, Matthew Berger Paper: https://pubmed.ncbi.nlm.nih.gov/37021295/ Code: https://github.com/MASILab/pyPheWAS Abstract Objective To enable interactive visualization of phenome-wide association studies (PheWAS) on electronic health records (EHR). Materials and Methods Current PheWAS technologies require familiarity with command-line interfaces and lack end-to-end data visualizations. pyPheWAS Explorer allows users to...
Predicting Crohn’s disease severity in the colon using mixed cell nucleus density from pseudo labels
Dec. 1, 2022—Lucas W. Remedios, Shunxing Bao, Cailey I. Kerley, Leon Y. Cai, François Rheault, Ruining Deng, Can Cui, Sophie Chiron, Ken S. Lau, Joseph T. Roland, Mary K. Washington, Lori A. Coburn, Keith T. Wilson, Yuankai Huo, Bennett A. Landman (2023). Predicting Crohn’s disease severity in the colon using mixed cell nucleus density from pseudo labels....
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
Jul. 25, 2022—Tang, Yucheng, Dong Yang, Wenqi Li, Holger R. Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh. “Self-supervised pre-training of swin transformers for 3d medical image analysis.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20730-20740. 2022. Full text: Abstract Vision Transformers (ViT)s have shown great performance in self-supervised...
pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis
Jan. 12, 2022—Kerley, C.I., Chaganti, S., Nguyen, T.Q. et al. pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis. Neuroinform (2022). https://doi.org/10.1007/s12021-021-09553-4 Full text: NIHMSID, Springer Abstract Along with the increasing availability of electronic medical record (EMR) data, phenome-wide association studies (PheWAS) and phenome-disease association studies (PheDAS) have become a prominent, first-line method of analysis for uncovering...
Inpainting Missing Tissue in Multiplexed Immunofluorescence Imaging
Dec. 10, 2021—Shunxing Bao, Yucheng Tang, Ho Hin Lee, Riqiang Gao, Qi Yang, Xin Yu, Sophie Chiron, Lori A. Coburn, Keith T. Wilson, Joseph T. Roland, Bennett A. Landman, Yuankai Huo. “Inpainting Missing Tissue in Multiplexed Immunofluorescence Imaging” Medical Imaging 2022: Digital and Computational Pathology. International Society for Optics and Photonics, accepted Full text: [TBD] Abstract Multiplex immunofluorescence (MxIF) is...
Efficient Quality Control with Mixed CT and CTA Datasets
Dec. 10, 2021—Lucas W. Remedios, Leon Y. Cai, Colin B. Hansen, Samuel W. Remedios, Bennett A. Landman (2022). Efficient Quality Control with Mixed CT and CTA Datasets. Proc SPIE Int Soc Opt Eng. 2022. Abstract Deep learning promises the extraction of valuable information from traumatic brain injury (TBI) datasets and depends on efficient navigation when using large-scale mixed computed tomography (CT) datasets from...
Automated, open-source segmentation of the Hippocampus and amygdala with the open Vanderbilt archive of the temporal lobe
Aug. 28, 2021—Plassard, Andrew J., Shunxing Bao*, Maureen McHugo, Lori Beason-Held, Jennifer U. Blackford, Stephan Heckers, and Bennett A. Landman. “Automated, open-source segmentation of the Hippocampus and amygdala with the open Vanderbilt archive of the temporal lobe.” Magnetic Resonance Imaging 81 (2021): 17-23. Full text: https://www.sciencedirect.com/science/article/abs/pii/S0730725X21000692 Abstract Examining volumetric differences of the amygdala and anterior-posterior regions of the...
Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records
Dec. 7, 2020—Yucheng Tang, Riqiang Gao, Ho Hin Lee, Quinn Stanton Wells, Ashley Spann, James Gregory Terry, Jeff Carr, Yuankai Huo, Shunxing Bao and Bennett A. Landman, “Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records”, MICCAI CLIP, 2020. Full Text: Abstract Type II diabetes mellitus (T2DM) is a significant public health concern...
Learning from dispersed manual annotations with an optimized data weighting policy
Dec. 7, 2020—Yucheng Tang, Riqiang Gao, Yunqiang Chen, Dashan Gao, Michael R. Savona, Richard G. Abramson, Shunxing Bao, Yuankai Huo and Bennett A. Landman, “Learning from Dispersed Manual Annotations with an Optimized Data Weighting Policy”, Journal of Medical Imaging, 2020. Full Text: Abstract https://pubmed.ncbi.nlm.nih.gov/32775501/ Purpose: Deep learning methods have become essential tools for quantitative interpretation of medical...