Research

You can also find my articles on my Google Scholar profile.

Anomaly detection in image or latent space of patch-based auto-encoders for industrial image analysis

Published in GRETSI, 2023

We study several methods for detecting anomalies in color images, constructed on patch-based auto-encoders. Wecompare the performance of three types of methods based, first, on the error between the original image and its reconstruction,second, on the support estimation of the normal image distribution in the latent space, and third, on the error between the originalimage and a restored version of the reconstructed image. These methods are evaluated on the industrial image database MVTecADand compared to two competitive state-of-the-art methods.

Nicolas Pinon, Robin Trombetta, Carole Lartizien (2023). "Anomaly detection in image or latent space of patch-based auto-encoders for industrial image analysis" XXIXème Colloque Francophone de Traitement du Signal et des Images.

One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities

Published in MIDL, 2023

Anomaly detection remains a challenging task in neuroimaging when little to no supervision is available and when lesions can be very small or with subtle contrast. Patch-based representation learning has shown powerful representation capacities when applied to industrial or medical imaging and outlier detection methods have been applied successfully to these images. In this work, we propose an unsupervised anomaly detection (UAD) method based on a latent space constructed by a siamese patch-based auto-encoder and perform the outlier detection with a One-Class SVM training paradigm tailored to the lesion detection task in multi-modality neuroimaging. We evaluate performances of this model on a public database, the White Matter Hyperintensities (WMH) challenge and show in par performance with the two best performing state-of-the-art methods reported so far.

Nicolas Pinon, Robin Trombetta, Carole Lartizien (2023). "One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities." Medical Imaging with Deep Learning.

Brain subtle anomaly detection based on auto-encoders latent space analysis: application to de novo parkinson patients

Published in ISBI, 2023

Neural network-based anomaly detection remains challenging in clinical applications with little or no supervised information and subtle anomalies such as hardly visible brain lesions. Among unsupervised methods, patch-based auto-encoders with their efficient representation power provided by their latent space, have shown good results for visible lesion detection. However, the commonly used reconstruction error criterion may limit their performance when facing less obvious lesions. In this work, we design two alternative detection criteria. They are derived from multivariate analysis and can more directly capture information from latent space representations. Their performance compares favorably with two additional supervised learning methods, on a difficult de novo Parkinson Disease (PD) classification task.

Nicolas Pinon, Geoffroy Oudoumanessah, Robin Trombetta, Michel Dojat, Florence Forbes, Carole Lartizien (2023). "Brain subtle anomaly detection based on auto-encoders latent space analysis: application to de novo parkinson patients." IEEE 20th International Symposium on Biomedical Imaging.

Improving motion‐mask segmentation in thoracic CT with multiplanar U‐nets

Published in Medical Physics, 2022

Motion‐mask segmentation from thoracic computed tomography (CT) images is the process of extracting the region that encompasses lungs and viscera, where large displacements occur during breathing. It has been shown to help image registration between different respiratory phases. This registration step is, for example, useful for radiotherapy planning or calculating local lung ventilation. Knowing the location of motion discontinuity, that is, sliding motion near the pleura, allows a better control of the registration preventing unrealistic estimates. Nevertheless, existing methods for motion‐mask segmentation are not robust enough to be used in clinical routine. This article shows that it is feasible to overcome this lack of robustness by using a lightweight deep‐learning approach usable on a standard computer, and this even without data augmentation or advanced model design.

Ludmilla Penarrubia, Nicolas Pinon, Emmanuel Roux, Eduardo Enrique Dávila Serrano, Jean‐Christophe Richard, Maciej Orkisz, David Sarrut (2022). "Improving motion‐mask segmentation in thoracic CT with multiplanar U‐nets." Medical Physics.

Patch vs. global image-based unsupervised anomaly detection in MR brain scans of early Parkinsonian patients

Published in MLCN (in conjonction with MICCAI), 2021

Although neural networks have proven very successful in a number of medical image analysis applications, their use remains difficult when targeting subtle tasks such as the identification of barely visible brain lesions, especially given the lack of annotated datasets. Good candidate approaches are patch-based unsupervised pipelines which have both the advantage to increase the number of input data and to capture local and fine anomaly patterns distributed in the image, while potential inconveniences are the loss of global structural information. We illustrate this trade-off on Parkinson’s disease (PD) anomaly detection comparing the performance of two anomaly detection models based on a spatial auto-encoder (AE) and an adaptation of a patch-fed siamese auto-encoder (SAE). On average, the SAE model performs better, showing that patches may indeed be advantageous.

Verónica Muñoz-Ramírez, Nicolas Pinon, Florence Forbes, Carole Lartizen, Michel Dojat (2021). "Patch vs. global image-based unsupervised anomaly detection in MR brain scans of early Parkinsonian patients" MLCN 2021 (in conjonction with MICCAI).