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Machine Learning Category

Learning site-invariant features of connectomes to harmonize complex network measures

Dec. 1, 2023—Figure 1. Previous research elucidated that connectomes suffer from confounding site effects. In this work we propose a data-driven model to learn disjoint site (ūĚĎź = {1,2}) and biological features (siteless z) for BIOCARD (orange) and VMAP (blue) (left). We then inject a prescribed site, c‚Äô, to the learned representations to compute harmonized connectome modularity,...

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Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation

Aug. 31, 2023—Ho Hin Lee, Yucheng Tang, Qi Yang, Xin Yu, Leon Y. Cai, Lucas W. Remedios, Shunxing Bao, Bennett A. Landman, Yuankai Huo Paper:¬†https://ieeexplore.ieee.org/document/10149329 Code:¬†https://github.com/MASILab/DCC_CL Abstract Medical image segmentation, or computing voxel-wise semantic masks, is a fundamental yet challenging task in medical imaging domain. To increase the ability of encoder-decoder neural networks to perform this task...

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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...

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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...

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Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context

May. 29, 2023—Leon Y. Cai, Ho Hin Lee, Nancy R. Newlin, Cailey I. Kerley, Praitayini Kanakaraj, Qi Yang, Graham W. Johnson, Daniel Moyer, Kurt G. Schilling, Fran√ßois Rheault, and Bennett A. Landman Paper:¬†https://www.biorxiv.org/content/10.1101/2023.02.25.530046v2 Code:¬†https://github.com/MASILab/cornn_tractography Abstract Diffusion MRI (dMRI) streamline tractography is the gold-standard for in vivo estimation of white matter (WM) pathways in the brain. However, the...

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Extending the value of routine lung screening CT with quantitative body composition assessment

Nov. 28, 2022—Kaiwen Xu, Riqiang Gao, Yucheng Tang, Steve A. Deppen, Kim L. Sandler, Michael N. Kammer, Sanja L. Antic, Fabien Maldonado, Yuankai Huo, Mirza S. Khan, Bennett A. Landman Abstract Certain body composition phenotypes, like sarcopenia, are well established as predictive markers for post-surgery complications and overall survival of lung cancer patients. However, their association with...

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Batch size: go big or go home? Counterintuitive improvement in medical autoencoders with smaller batch size

Nov. 13, 2022—Cailey I. Kerley*, Leon Y. Cai*, Yucheng Tang, Lori L. Beason-Held, Susan M. Resnick,¬†Laurie E. Cutting, and Bennett A. Landman. *Equal first authorship Abstract Batch size is a key hyperparameter in training deep learning models. Conventional wisdom suggests larger batches produce improved model performance. Here we present evidence to the contrary, particularly when using autoencoders...

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3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation

Oct. 6, 2022—Ho Hin Lee, Shunxing Bao, Yuankai Huo, Bennett A. Landman, “3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation”, arXiv 2022 Full Text Abstract Vision transformers (ViTs) have quickly superseded convolutional networks (ConvNets) as the current state-of-the-art (SOTA) models for medical image segmentation. Hierarchical transformers (e.g., Swin Transformers) reintroduced several...

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Generalizing deep learning brain segmentation for skull removal and intracranial measurements

Jul. 25, 2022—Yue Liu, Yuankai Huo, Blake Dewey, Ying Wei, Ilwoo Lyu, Bennett A. Landman ,‚ÄúGeneralizing deep learning brain segmentation for skull removal and intracranial measurements.‚ÄĚMagnetic Resonance Imaging. Volume 88, May 2022, Pages 44-52 Full Text Abstract Total intracranial volume (TICV) and posterior fossa volume (PFV) are essential covariates for brain volumetric analyses with structural magnetic resonanceimaging...

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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...

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