Longitudinal Category

Longitudinal changes of connectomes and graph theory measures in aging

Jan. 17, 2022—Yuzhe Wang, Francois Rheault, Kurt G. Schilling, Lori L. Beason-Held, Andrea T. Shafer, Susan M. Resnick, Bennett A. Landman Abstract Changes in brain structure and connectivity in aging can be probed through diffusion weighted MRI and summarized with structural connectome matrices. Complex network analysis based on graph theory has been applied to provide measures that...

Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective

Aug. 28, 2021—Riqiang Gao, Yucheng Tang, Kaiwen Xu, Ho Hin Lee, Steve Deppen, Kim Sandler, Pierre Massion, Thomas A. Lasko, Yuankai Huo, Bennett A. Landman, Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective, MICCAI, 2021. Full text: https://arxiv.org/abs/2107.11882 Abstract Data from multi-modality provide complementary information in clinical prediction, but missing data in clinical cohorts limits...

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...

The base regression neural network is depicted in the center which describes the parameters of the fullyconnected dense layers with respective activation functions. The plot at left: Depicts three inputs where the center input
comes from corresponding DW-MRI with histology. The other two are pairwise inputs from corresponding voxels of
scanner 1.5T and 3T. The plot at right: Depicts the loss function which uses the hypothesis that the outcome/prediction
should be same irrespective of the scanner gradient strength.

Harmonizing 1.5 T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators

Jan. 17, 2020—Nath V, Remedios S, Parvathaneni P, Hansen CB, Bayrak RG, Bermudez C, Blaber JA, Schilling KG, Janve VA, Gao Y, Huo Y. Harmonizing 1.5 T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators. In Medical Imaging 2019: Image Processing 2019 Mar 15 (Vol. 10949, p. 109490O). International Society for Optics and Photonics....

The framework of DLSTM (three “steps” in the example). x_t is the input data at time point t, and d_t is the time distance from the time point t to the latest time point. “F” represents the learnable DLSTM component (convolutional version in this paper). H_t and C_t are the hidden state and cell state, respectively. The input data, x_t, could be 1D, 2D, or 3D.

Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection

Oct. 13, 2019—Gao, R., Huo, Y., Bao, S., Tang, Y., Antic, S.L., Epstein, E.S., Balar, A.B., Deppen, S., Paulson, A.B., Sandler, K.L. and Massion, P.P., 2019, October. Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection. In International Workshop on Machine Learning in Medical Imaging (pp. 310-318). Springer, Cham. [Full Text] Abstract The field of...