Skip to main content

Deep Learning Category

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....

Read more


SynBOLD-DisCo: Synthetic BOLD images for distortion correction of fMRI without additional calibration scans

Nov. 13, 2022—Tian Yu*, Leon Y. Cai*, Victoria L. Morgan, Sarah E. Goodale, Dario J. Englot, Catherine E. Chang, Bennett A. Landman, and Kurt G. Schilling * Equal first authorship https://github.com/MASILab/SynBOLD-DisCo Abstract The blood oxygen level dependent (BOLD) signal from functional magnetic resonance imaging (fMRI) is a noninvasive technique that has been widely used in research to...

Read more


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...

Read more


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Transformer for Medical Image Segmentation”, arXiv 2022 Full Text Abstract Vision transformers (ViTs) have quickly superseded convolutional networks (ConvNets) as the current state-of-the-art (SOTA) models for medical image segmentation.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...

Read more


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...

Read more


Label efficient segmentation of single slice thigh CT with two-stage pseudo labels

Jul. 25, 2022—Qi Yang, Xin Yu, Ho Hin Lee, Yucheng Tang, Shunxing Bao,Kristofer S. Gravenstein, Ann Zenobia Moore, Sokratis Makrogiannis, Luigi Ferrucci, and Bennett A. Landman. “Label efficient segmentation of single slice thigh CT with two-stage pseudo labels” Journal of Medical Imaging, 2022 Purpose: Muscle, bone, and fat segmentation from thigh images is essential for quantifying body...

Read more


Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models

Jul. 25, 2022—Xin Yu*, Qi Yang*, Yucheng Tang, Riqiang Gao, Shunxing Bao, Leon Y. Cai, Ho Hin Lee, Ann Zenobia Moore, Luigi Ferrucci, Bennett A. Landman, “Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models”, MICCAI 2022   2D low-dose single-slice abdominal computed tomography (CT) slice enables direct measurements of body composition, which...

Read more


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 ,“Generalizing deep learning brain segmentation for skull removal and intracranial measurements.”Magnetic 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...

Read more


High-resolution 3D abdominal segmentation with random patch network fusion

Dec. 14, 2021—Y. Tang,R.Gao,S.Han, Y.Chen, D.Gao, V.Nath, C.Bermudez, M.R. Savona, R.G. Abramson, S.Bao,I.Lyu, Y.Huo and B.A. Landman,“High-resolution 3D Abdominal Segmentation with Random PatchNetworkFusion”,Medical Image Analysis, 2021. Full Text: https://www.sciencedirect.com/science/article/pii/S1361841520302589 Abstract Deep learning for three dimensional (3D) abdominal organ segmentation on high-resolution computed to- mography (CT) is a challenging topic, in part due to the limited memory provide...

Read more


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...

Read more