Deep Learning Category
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...
Technical Note: Comparison of Convolutional Neural Networks for Detecting Large Vessel Occlusion on Computed Tomography Angiography
Jul. 21, 2021—Lucas W. Remedios, Sneha Lingam, Samuel W. Remedios, Riqiang Gao, Stephen W. Clark, Larry T. Davis, Bennett A. Landman. Technical Note: Comparison of Convolutional Neural Networks for Detecting Large Vessel Occlusion on Computed Tomography Angiography. Medical Physics, 2021 Full Text Abstract Purpose: Artificial intelligence diagnosis and triage of large vessel occlusion may quicken clinical response for...
Labeling Lateral Prefrontal Sulci using Spherical Data Augmentation and Context-aware Training
Apr. 1, 2021—Ilwoo Lyu, Shunxing Bao, Lingyan Hao, Jewelia Yao, Jacob Miller, Willa Voorhies, Warren Taylor, Silvia Bunge, Kevin Weiner, Bennett Landman. “Labeling Lateral Prefrontal Sulci using Spherical Data Augmentation and Context-aware Training”. NeuroImage, 229, 117758, 2021. [Full text][Code] Abstract The inference of cortical sulcal labels often focuses on deep (primary and secondary) sulcal regions, whereas shallow...
Body Part Regression With Self-Supervision
Jan. 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,”Body Part Regression with Self-supervision”,IEEETransactions onMedicalImaging,2021 Full Text: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9350603 Abstract Body part regression is a promising new technique that enables content navigation through selfsupervised learning. Using this technique, the global quantitative spatial location for each axial view slice is obtained...
Generalizing deep whole-brain segmentation for post-contrast MRI with transfer learning
Dec. 26, 2020—Bermudez, C., Remedios, S. W., Ramadass, K., McHugo, M., Heckers, S., Huo, Y., & Landman, B. A. (2020). Generalizing deep whole-brain segmentation for post-contrast MRI with transfer learning. Journal of Medical Imaging, 7(6), 064004. Full Text: https://pubmed.ncbi.nlm.nih.gov/33381612/ Abstract Purpose: Generalizability is an important problem in deep neural networks, especially with variability of data acquisition in...
Deep Multi-path Network Integrating Incomplete Biomarker and Chest CT Data for Evaluating Lung Cancer Risk
Nov. 25, 2020—Riqiang Gao, Yucheng Tang, Kaiwen Xu, Michael N. Kammer, Sanja L. Antic, Steve Deppen, Kim L. Sandler, Pierre P. Massion, Yuankai Huo, Bennett A. Landman,Deep Multi-path Network Integrating Incomplete Biomarker and Chest CT Data for Evaluating Lung Cancer Risk, SPIE, Medical Imaging, 2021. Full text: https://arxiv.org/abs/2010.09524 Abstract Clinical data elements (CDEs) (e.g., age, smoking history), blood...
Multi-path x-D Recurrent Neural Networks for Collaborative Image Classification
Nov. 25, 2020—Riqiang Gao, Yuankai Huo, Shunxing Bao, Yucheng Tang, Sanja L. Antic, Emily S. Epstein, Steve Deppen, Alexis B. Paulson, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman,Multi-path x-D Recurrent Neural Networks for Collaborative Image Classification. Neurocomputing, 2020. Full Text: https://doi.org/10.1016/j.neucom.2020.02.033 Abstract With the rapid development of image acquisition and storage, multiple images per class are...
Time-Distanced Gates in Long Short-Term Memory Networks
Nov. 25, 2020—Gao, R., Tang, Y., Xu, K., Huo, Y., Bao, S., Antic, S.L., Epstein, E.S., Deppen, S., Paulson, A.B., Sandler, K.L. and Massion, P.P., Landman, B. A., Time-distanced gates in long short-term memory networks. Medical Image Analysis, 2020. Full Text: https://pubmed.ncbi.nlm.nih.gov/32745977/ Abstract The Long Short-Term Memory (LSTM) network is widely used in modeling sequential observations in fields ranging...
Renal Cortex, Medulla and Pelvicaliceal System Segmentation on Arterial Phase CT Images with Random Patch-based Networks
Nov. 21, 2020—Yucheng Tang, Riqiang Gao, Ho Hin Lee, Brent V. Savoie, Shunxing Bao, Yuankai Huo, Jeffrey Spraggins and Bennett A, Landman, Renal Cortex, Medulla, Pelvis Segmentation on Arterial Phase CT Images with Random Patch-based Networks, SPIE 2021 Medical Imaging Full Text Abstract Renal segmentation on contrast-enhanced computed tomography (CT) provides distinct spatial context and morphology. Current studies for renal segmentations...