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Time-distanced gates in long short-term memory networks.

Riqiang Gao1, Yucheng Tang1, Kaiwen Xu1

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|August 4, 2020
PubMed
Summary

The Distanced Long Short-Term Memory (DLSTM) network improves lung cancer diagnosis by explicitly modeling time intervals between computed tomography (CT) scans, outperforming traditional models on both regular and irregular data.

Keywords:
Distanced LSTMLongitudinalLung cancer diagnosisTemporal Emphasis Model

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging Analysis
  • Machine Learning for Healthcare

Background:

  • Long Short-Term Memory (LSTM) networks are used in medical imaging, including computed tomography (CT) for lung screening.
  • Traditional LSTMs overlook time intervals between scans, while existing variants struggle with temporal proximity and irregular sampling common in clinical practice.

Purpose of the Study:

  • To introduce the Distanced Long Short-Term Memory (DLSTM) network with a Temporal Emphasis Model (TEM) for improved lung cancer diagnosis.
  • To address limitations in modeling temporal proximity and irregular sampling in sequential medical imaging data.

Main Methods:

  • Developed DLSTM by incorporating time-distanced gates (input and forget) to explicitly model the time difference to the last scan.
  • Integrated a Temporal Emphasis Model (TEM) to prioritize newer scans in longitudinal data.
  • Evaluated DLSTM using simulated data and real CT images from the National Lung Screening Trial (NLST) and clinical studies.

Main Results:

  • DLSTM captured temporal relationships missed by traditional LSTMs in simulated data.
  • DLSTM achieved superior performance on both regularly and irregularly sampled CT data, improving F1 score from 0.6785 to 0.7085 in NLST.
  • External validation on irregularly acquired data showed DLSTM achieving an AUC score of 0.8905, surpassing benchmarks (0.8350-0.8380).

Conclusions:

  • DLSTM is compatible with various temporal models (linear, quadratic, exponential, log-exponential).
  • The DLSTM approach enhances lung nodule malignancy evaluation by effectively handling temporal dynamics in medical imaging.
  • DLSTM offers a scalable solution for improving diagnostic accuracy in longitudinal medical data analysis without significant complexity increase.