MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction

by   Changhee Han, et al.

Unsupervised learning can discover various unseen diseases, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence Magnetic Resonance Imaging (MRI) scans. Therefore, we propose unsupervised Medical Anomaly Detection Generative Adversarial Network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect various diseases at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 L1 loss-trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones-reconstructs unseen healthy/abnormal scans; (Diagnosis) Average L2 loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use 1,133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans. Our Self-Attention MADGAN can detect AD on T1 scans at a very early stage, Mild Cognitive Impairment (MCI), with Area Under the Curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921.


page 1

page 9

page 10

page 12

page 13

page 14


GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis

Leveraging large-scale healthy datasets, unsupervised learning can disco...

Outlier-based Autism Detection using Longitudinal Structural MRI

Diagnosis of Autism Spectrum Disorder (ASD) using clinical evaluation (c...

3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI

Modern deep unsupervised learning methods have shown great promise for d...

Transformer-based normative modelling for anomaly detection of early schizophrenia

Despite the impact of psychiatric disorders on clinical health, early-st...

Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data

The detection of lesions in magnetic resonance imaging (MRI)-scans of hu...

AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection

Robust and accurate detection and segmentation of heterogenous tumors ap...

Structure Guided Manifolds for Discovery of Disease Characteristics

In medical image analysis, the subtle visual characteristics of many dis...

Please sign up or login with your details

Forgot password? Click here to reset