Anomaly Detection on Medical Images using Autoencoder and Convolutional Neural Network

Abstract

Detection of anomalies from the medical image dataset improves prognosis by discovering new facts hidden in the data. The present study aims to discuss anomaly detection using autoencoders and convolutional neural networks. The autoencoder identifies the imbalance between normal and abnormal samples. They create learning models flexible and accurate on training data. The problem is addressed in four stages: 1) training: an autoencoder is initialized with the hyperparameters and trained on the lung cancer CT scan images, 2) test: the autoencoder reconstructs the input from the latent space representation with a slight variation from the original data, indicated by a reconstruction error as Mean Squared Error (MSE), 3) evaluate: the MSE value of the training and test dataset are compared. The MSE values of anomalous data are higher than a base threshold, detecting those as anomalies, 4) validate: the efficiency metrics such as accuracy and MSE scores are used at both training and validation phases. The dataset was further classified as benign and malignant. The accuracy reported for outlier detection and the classification task is 98% and 97.2%. Thus, the proposed autoencoder-based anomaly detection could positively isolate anomalies from the CT scan images of lung cancer.

Learning Optimal White Matter Tract Representations from Tractography using a Deep Generative Model for Population Analyses

“…Autoencoder-based approaches have been used in unsupervised anomaly detection for medical images. 31, 32 The generative nature of the ConvVAE makes it possible to perform inference on new data, and any point in the smooth latent space can generate meaningful decodings instead of only minimizing reconstruction loss. In the context of anomaly detection when incoming data can have high variability and project to points on the latent space far from those of the training data, this quality of ConvVAE allows us to use the decoded output for outlier rejection and denoising.…”

Enhanced Diagnostic Accuracy in Musculoskeletal Radiography: A Comprehensive Ensemble Approach of Deep Learning Models

“…The images are first preprocessed and then trained using autoencoders in which images are designed to receive an input and transform it into a different representation and use various CNN models which are later evaluated with various metrics. The results showed that the proposed approach is worthwhile in detecting anomalies in the CT scan images with an accuracy of 98% and low MSE(Mean Squared Error) [18].…”

Learning optimal white matter tract representations from tractography using a deep generative model for population analyses

“…Autoencoder-based approaches have been used in unsupervised anomaly detection for medical images. 31,32 The generative nature of the ConvVAE makes it possible to perform inference on new data, and any point in the smooth latent space can generate meaningful decodings instead of only minimizing reconstruction loss. In the context of anomaly detection when incoming data can have high variability and project to points on the latent space far from those of the training data, this quality of ConvVAE allows us to use the decoded output for outlier rejection and denoising.…”