ArticleMedical image analysis2020
Time-distanced gates in long short-term memory networks.
Article in Medical image analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Cohort-Aware Agents for Individualized Lung Cancer Risk Prediction Using a Retrieval-Augmented Model Selection Framework.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Enhancing AI-driven forecasting of diabetes burden: a comparative analysis of deep learning and statistical models.Scientific reports · 2025Article
- Multimodal integration of longitudinal noninvasive diagnostics for survival prediction in immunotherapy using deep learning.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Performance of Lung Cancer Prediction Models for Screening-detected, Incidental, and Biopsied Pulmonary Nodules.Radiology. Artificial intelligence · 2025Article
- Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Deep Learning for Medical Image-Based Cancer Diagnosis.Cancers · 2023Review
- MSTS-Net: malignancy evolution prediction of pulmonary nodules from longitudinal CT images via multi-task spatial-temporal self-attention network.International journal of computer assisted radiology and surgery · 2023Article
- The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review.Biosensors · 2023Review
- Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Reducing uncertainty in cancer risk estimation for patients with indeterminate pulmonary nodules using an integrated deep learning model.Computers in biology and medicine · 2022Article
- Longitudinal prediction of lung nodule invasiveness by sequential modelling with common clinical computed tomography (CT) measurements: a prediction accuracy study.Translational lung cancer research · 2022Article
- Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study.Translational lung cancer research · 2022Article
- Deep Learning-Based Long Term Mortality Prediction in the National Lung Screening Trial.IEEE access : practical innovations, open solutions · 2022Article
- The impact of the lung EDRN-CVC on Phase 1, 2, & 3 biomarker validation studies.Cancer biomarkers : section A of Disease markers · 2022Article
- Deep learning for predicting COVID-19 malignant progression.Medical image analysis · 2021Article
- Deep Multi-path Network Integrating Incomplete Biomarker and Chest CT Data for Evaluating Lung Cancer Risk.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Development and Characterization of a Chest CT Atlas.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Extending the value of routine lung screening CT with quantitative body composition assessment.Proceedings of SPIE--the International Society for Optical EngineeringArticle
- Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
The Long Short-Term Memory (LSTM) network is widely used in modeling sequential observations in fields ranging from natural language processing to medical imaging. The LSTM has shown promise for interpreting computed tomography (CT) in lung screening protocols. Yet, traditional image-based LSTM models ignore interval differences, while recently proposed interval-modeled LSTM variants are limited in their ability to interpret temporal proximity. Meanwhile, clinical imaging acquisition may be irregularly sampled, and such sampling patterns may be commingled with clinical usages. In this paper, we propose the Distanced LSTM (DLSTM) by introducing time-distanced (i.e., time distance to the last scan) gates with a temporal emphasis model (TEM) targeting at lung cancer diagnosis (i.e., evaluating the malignancy of pulmonary nodules). Briefly, (1) the time distance of every scan to the last scan is modeled explicitly, (2) time-distanced input and forget gates in DLSTM are introduced across regular and irregular sampling sequences, and (3) the newer scan in serial data is emphasized by the TEM. The DLSTM algorithm is evaluated with both simulated data and real CT images (from 1794 National Lung Screening Trial (NLST) patients with longitudinal scans and 1420 clinical studied patients). Experimental results on simulated data indicate the DLSTM can capture families of temporal relationships that cannot be detected with traditional LSTM. Cross-validation on empirical CT datasets demonstrates that DLSTM achieves leading performance on both regularly and irregularly sampled data (e.g., improving LSTM from 0.6785 to 0.7085 on F1 score in NLST). In external-validation on irregularly acquired data, the benchmarks achieved 0.8350 (CNN feature) and 0.8380 (with LSTM) on AUC score, while the proposed DLSTM achieves 0.8905. In conclusion, the DLSTM approach is shown to be compatible with families of linear, quadratic, exponential, and log-exponential temporal models. The DLSTM can be readily extended with other temporal dependence interactions while hardly increasing overall model complexity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.