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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Self-Supervised Anomaly Detection from Anomalous Training Data via Iterative Latent Token Masking.

Ashay Patel¹, Petru-Daniel Tudosiu¹, Walter H L Pinaya¹

¹King's College London.

... IEEE International Conference on Computer Vision Workshops. IEEE International Conference on Computer Vision

|August 29, 2024

Summary

This summary is machine-generated.

This study introduces Iterative Latent Token Masking, a novel self-supervised framework for anomaly detection. It enables training models on datasets with anomalous images, outperforming existing methods in computer vision and medical imaging.

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Area of Science:

Computer Vision
Machine Learning
Medical Imaging Analysis

Background:

Anomaly detection is crucial across various fields, including medical imaging and industrial quality control.
Existing unsupervised methods often fail when training data contains anomalies, a common issue in medical imaging.
Current state-of-the-art models struggle with even small proportions of anomalous data during self-supervised training.

Purpose of the Study:

To propose a self-supervised framework for anomaly detection capable of training on datasets with anomalous images.
To overcome the limitations of current unsupervised and self-supervised anomaly detection methods when faced with contaminated training data.
To adapt robust statistics principles, specifically M-estimators, for iterative model fitting in anomaly detection.

Main Methods:

Introduced Iterative Latent Token Masking, a self-supervised framework leveraging Transformers and Vector Quantized-Variational Autoencoders.
Utilized token masking capabilities of Transformers to iteratively filter anomalous tokens within the training set.
Adapted iterative model fitting with M-estimators from robust statistics for unsupervised anomaly detection.

Main Results:

Demonstrated superior performance over state-of-the-art models on whole-body PET data and the MVTec Dataset.
Showcased the framework's effectiveness across varying levels of anomalous data in training sets.
Highlighted the limitations of current self-supervised, self-trained, and unsupervised models with anomalous training data.

Conclusions:

Iterative Latent Token Masking effectively addresses the challenge of training anomaly detection models on contaminated datasets.
The proposed method offers a robust alternative for unsupervised anomaly detection, particularly in scenarios with unavoidable anomalies.
This approach shows significant potential for advancing anomaly detection in both medical imaging and computer vision applications.