DeepAI AI Chat
Log In Sign Up

Unsupervised anomaly detection for a Smart Autonomous Robotic Assistant Surgeon (SARAS)using a deep residual autoencoder

by   Dinesh Jackson Samuel, et al.
Oxford Brookes University

Anomaly detection in Minimally-Invasive Surgery (MIS) traditionally requires a human expert monitoring the procedure from a console. Data scarcity, on the other hand, hinders what would be a desirable migration towards autonomous robotic-assisted surgical systems. Automated anomaly detection systems in this area typically rely on classical supervised learning. Anomalous events in a surgical setting, however, are rare, making it difficult to capture data to train a detection model in a supervised fashion. In this work we thus propose an unsupervised approach to anomaly detection for robotic-assisted surgery based on deep residual autoencoders. The idea is to make the autoencoder learn the 'normal' distribution of the data and detect abnormal events deviating from this distribution by measuring the reconstruction error. The model is trained and validated upon both the publicly available Cholec80 dataset, provided with extra annotation, and on a set of videos captured on procedures using artificial anatomies ('phantoms') produced as part of the Smart Autonomous Robotic Assistant Surgeon (SARAS) project. The system achieves recall and precision equal to 78.4 on the SARAS phantom dataset. The end-to-end system was developed and deployed as part of the SARAS demonstration platform for real-time anomaly detection with a processing time of about 25 ms per frame.


page 2

page 3

page 8


Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data

Due to the growing amount of data from in-situ sensors in wastewater sys...

Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination

In this paper, we propose Normality-Calibrated Autoencoder (NCAE), which...

Anomaly Detection using Autoencoders in High Performance Computing Systems

Anomaly detection in supercomputers is a very difficult problem due to t...

Mirrored Autoencoders with Simplex Interpolation for Unsupervised Anomaly Detection

Use of deep generative models for unsupervised anomaly detection has sho...

Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder

In data systems, activities or events are continuously collected in the ...

Anomaly Detection in Beehives using Deep Recurrent Autoencoders

Precision beekeeping allows to monitor bees' living conditions by equipp...

Using UMAP to Inspect Audio Data for Unsupervised Anomaly Detection under Domain-Shift Conditions

The goal of Unsupervised Anomaly Detection (UAD) is to detect anomalous ...