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Found 7 papers, 2 papers with code
Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT
Image Quality Assessment (IQA) is important for scientific inquiry, especially in medical imaging and machine learning.
Development and Characterization of a Chest CT Atlas
For the entire study cohort, the optimized pipeline achieves a registration success rate of 91. 7%.
Deep Multi-path Network Integrating Incomplete Biomarker and Chest CT Data for Evaluating Lung Cancer Risk
The network can be trained end-to-end with both medical image features and CDEs/biomarkers, or make a prediction with single modality.
Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection
To improve performance on the primary task, we propose an Internal-Transfer Weighting (ITW) strategy to suppress the loss functions on auxiliary tasks for the final stages of training.
Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging
Annual low dose computed tomography (CT) lung screening is currently advised for individuals at high risk of lung cancer (e. g., heavy smokers between 55 and 80 years old).
Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection
To model both regular and irregular longitudinal samples, we generalize the LSTM model with the Distanced LSTM (DLSTM) for temporally varied acquisitions.
Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics
The results show that the AUC obtained from clinical demographics alone was 0. 635 while the attention network alone reached an accuracy of 0. 687.
