Missing data imputation in meteorological datasets with the GAIN method

Popolizio, Marina; Amato, Alberto; Politi, T.; Calienno, Roberto; Lecce, Vincenzo Di

doi:10.1109/metroind4.0iot51437.2021.9488451

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…Different data imputation models are used to estimate missing vessel segments. These models include the multivariate imputation by chained equations (MICE) [54], GAIN [55], auto-encoder (AE) [56], L2 regularized regression (L2RR) [57], reinforcement learningbased approach (RL) [58], Neural Network Gaussian Process (NNGP) [59], probabilistic nearest-neighbor (PNN) [60], and modified GAIN [61]. The best model is selected according to the error value of the root mean square (RMSE) and Freshet Inception Distance (FID).…”

Section: Dynamic Data Imputationmentioning

confidence: 99%

Multi-Layer Preprocessing and U-Net with Residual Attention Block for Retinal Blood Vessel Segmentation

Alsayat,

Elmezain,

Alanazi

et al. 2023

Diagnostics

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Retinal blood vessel segmentation is a valuable tool for clinicians to diagnose conditions such as atherosclerosis, glaucoma, and age-related macular degeneration. This paper presents a new framework for segmenting blood vessels in retinal images. The framework has two stages: a multi-layer preprocessing stage and a subsequent segmentation stage employing a U-Net with a multi-residual attention block. The multi-layer preprocessing stage has three steps. The first step is noise reduction, employing a U-shaped convolutional neural network with matrix factorization (CNN with MF) and detailed U-shaped U-Net (D_U-Net) to minimize image noise, culminating in the selection of the most suitable image based on the PSNR and SSIM values. The second step is dynamic data imputation, utilizing multiple models for the purpose of filling in missing data. The third step is data augmentation through the utilization of a latent diffusion model (LDM) to expand the training dataset size. The second stage of the framework is segmentation, where the U-Nets with a multi-residual attention block are used to segment the retinal images after they have been preprocessed and noise has been removed. The experiments show that the framework is effective at segmenting retinal blood vessels. It achieved Dice scores of 95.32, accuracy of 93.56, precision of 95.68, and recall of 95.45. It also achieved efficient results in removing noise using CNN with matrix factorization (MF) and D-U-NET according to values of PSNR and SSIM for (0.1, 0.25, 0.5, and 0.75) levels of noise. The LDM achieved an inception score of 13.6 and an FID of 46.2 in the augmentation step.

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