Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET
Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has
gained in popularity. This approach has the great advantage of not requiring tedious pixel …
gained in popularity. This approach has the great advantage of not requiring tedious pixel …
Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT
Objectives To quantify regional manifestations related to COPD as anomalies from a
modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) …
modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) …
Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks
There is a growing interest in single-class modelling and out-of-distribution detection as fully
supervised machine learning models cannot reliably identify classes not included in their …
supervised machine learning models cannot reliably identify classes not included in their …
[PDF][PDF] Prototype-Aware Contrastive Knowledge Distillation for Few-Shot Anomaly Detection.
Abstract Knowledge distillation (KD) is widely adopted in anomaly detection but how to
extend it to the few-shot setting, where a few normal samples are provided for detecting …
extend it to the few-shot setting, where a few normal samples are provided for detecting …
Pseudo-healthy image reconstruction with variational autoencoders for anomaly detection: A benchmark on 3D brain FDG PET
Many deep generative models have been proposed to reconstruct pseudo-healthy images
for anomaly detection. Among these models, the variational autoencoder (VAE) has …
for anomaly detection. Among these models, the variational autoencoder (VAE) has …