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Found 13 papers, 4 papers with code
Investigating the impact of kernel harmonization and deformable registration on inspiratory and expiratory chest CT images for people with COPD
Results show harmonization significantly reduces emphysema measurement inconsistencies, decreasing median emphysema scores from 10. 479% to 3. 039%, with a reference median score of 1. 305% from the STANDARD kernel as the target.
Beyond the Lungs: Extending the Field of View in Chest CT with Latent Diffusion Models
Once trained, our approach extends the FOV of CT images in the z-direction by generating new axial slices in a zero-shot manner.
No winners: Performance of lung cancer prediction models depends on screening-detected, incidental, and biopsied pulmonary nodule use cases
This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose computed tomography, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy.
Inter-vendor harmonization of Computed Tomography (CT) reconstruction kernels using unpaired image translation
In this study, we adopt an unpaired image translation approach to investigate harmonization between and across reconstruction kernels from different manufacturers by constructing a multipath cycle generative adversarial network (GAN).
Zero-shot CT Field-of-view Completion with Unconditional Generative Diffusion Prior
Anatomically consistent field-of-view (FOV) completion to recover truncated body sections has important applications in quantitative analyses of computed tomography (CT) with limited FOV.
Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification
In this work, we propose a transformer-based multimodal strategy to integrate repeat imaging with longitudinal clinical signatures from routinely collected EHRs for SPN classification.
Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension Perspective
We evaluate the validity of the proposed method in automatic BC assessment using lung screening CT with limited FOV.
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.
