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Cancer Risk Estimation Combining Lung Screening CT with Clinical Data Elements

Dec. 9, 2021—Riqiang Gao, Yucheng Tang, Mirza S. Khan, Kaiwen Xu, Alexis B. Paulson, Shelbi Sullivan, Yuankai Huo, Stephen Deppen, Pierre P. Massion, Kim L. Sandler, Bennett A. Landman, Cancer Risk Estimation Combining Lung Screening CT with Clinical Data Elements, Radiology: Artificial Intelligence (2021). Full Text: https://pubs.rsna.org/doi/10.1148/ryai.2021210032 Abstract Purpose: To develop a model to estimate lung cancer risk using lung...

Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT

Aug. 28, 2021—Riqiang Gao, Mirza S. Khan, Yucheng Tang, Kaiwen Xu, Steve Deppen, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman, Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT, Technical report, 2021. Full text: https://arxiv.org/abs/2107.12842 Abstract Image Quality Assessment (IQA) is important for scientific inquiry, especially in medical imaging and machine learning....

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...

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...

Example of Internal-transfer Weighting (ITW) of multi-task learning. The LC, AA, CB, COPD, E represent the tasks of lung cancer, adult asthma, chronic bronchitis, chronic obstructive pulmonary disease and emphysema, respectively. The red number is the loss weight for the task. Note that we apply PFLP on top of the displayed weights.

Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection

Dec. 5, 2019—Yiyuan Yang, Riqiang Gao*, Yucheng Tang, Sanja L. Antic, Steve Deppen, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman, “Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection,” SPIE MI:IP 2020. Houston, TX. [full text] Abstract Deep learning has achieved many successes in medical imaging, including lung nodule segmentation and lung cancer prediction on...

Vision for joint consideration of diagnosis and cancer-free progression time (CFPT) in cancer and non-cancer patients. The blue dashed lines represent the defined CFPT. The red number (%) in small rectangle is the risk score computed by the PLCOm2012 [3] model. Current scores suggest screening at every point since they indicate high risk (>= 2%). If CFPT were known, the green rectangles could have been avoided.

Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging

Dec. 5, 2019—Riqiang Gao, Lingfeng Li, Yucheng Tang, Sanja L. Antic, Alexis B. Paulson, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman, “Deep Multi- task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging,” SPIE MI:IP 2020. Houston, TX. [full text] Abstract Annual low dose computed tomography (CT) lung screening is currently advised for individuals...

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...