Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset

In recent years, the industrial sector has evolved towards its fourth revolution. The quality control domain is particularly interested in advanced machine learning for computer vision anomaly detection. Nevertheless, several challenges have to be faced, including imbalanced datasets, the image complexity, and the zero-false-negative (ZFN) constraint to guarantee the high-quality requirement. This paper illustrates a use case for an industrial partner, where Printed Circuit Board Assembly (PCBA) images are first reconstructed with a Vector Quantized Generative Adversarial Network (VQGAN) trained on normal products. Then, several multi-level metrics are extracted on a few normal and abnormal images, highlighting anomalies through reconstruction differences. Finally, a classifer is trained to build a composite anomaly score thanks to the metrics extracted. This three-step approach is performed on the public MVTec-AD datasets and on the partner PCBA dataset, where it achieves a regular accuracy of 95.69% and 87.93% under the ZFN constraint

Real time web-based toolbox for computer vision

The last few years have been strongly marked by the presence of multimedia data (images and videos) in our everyday lives. These data are characterized by a fast frequency of creation and sharing since images and videos can come from different devices such as cameras, smartphones or drones. The latter are generally used to illustrate objects in different situations (airports, hospitals, public areas, sport games, etc.). As result, image and video processing algorithms have got increasing importance for several computer vision applications such as motion tracking, event detection and recognition, multimedia indexation and medical computer-aided diagnosis methods. In this paper, we propose a real time cloud-based toolbox (platform) for computer vision applications. This platform integrates a toolbox of image and video processing algorithms that can be run in real time and in a secure way. The related libraries and hardware drivers are automatically integrated and configured in order to offer to users an access to the different algorithms without the need to download, install and configure software or hardware. Moreover, the platform offers the access to the integrated applications from multiple users thanks to the use of Docker (Merkel, 2014) containers and images. Experimentations were conducted within three kinds of algorithms: 1. image processing toolbox. 2. Video processing toolbox. 3. 3D medical methods such as computer-aided diagnosis for scoliosis and osteoporosis.  These experimentations demonstrated the interest of our platform for sharing our scientific contributions related to computer vision domain. The scientific researchers could be able to develop and share easily their applications fastly and in a safe way

Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study

Predicting response to neoadjuvant therapy is a vexing challenge in breast cancer. In this study, we evaluate the ability of deep learning to predict response to HER2-targeted neo-adjuvant chemotherapy (NAC) from pre-treatment dynamic contrast-enhanced (DCE) MRI acquired prior to treatment. In a retrospective study encompassing DCE-MRI data from a total of 157 HER2+ breast cancer patients from 5 institutions, we developed and validated a deep learning approach for predicting pathological complete response (pCR) to HER2-targeted NAC prior to treatment. 100 patients who received HER2-targeted neoadjuvant chemotherapy at a single institution were used to train (n=85) and tune (n=15) a convolutional neural network (CNN) to predict pCR. A multi-input CNN leveraging both pre-contrast and late post-contrast DCE-MRI acquisitions was identified to achieve optimal response prediction within the validation set (AUC=0.93). This model was then tested on two independent testing cohorts with pre-treatment DCE-MRI data. It achieved strong performance in a 28 patient testing set from a second institution (AUC=0.85, 95% CI 0.67-1.0, p=.0008) and a 29 patient multicenter trial including data from 3 additional institutions (AUC=0.77, 95% CI 0.58-0.97, p=0.006). Deep learning-based response prediction model was found to exceed a multivariable model incorporating predictive clinical variables (AUC < .65 in testing cohorts) and a model of semi-quantitative DCE-MRI pharmacokinetic measurements (AUC < .60 in testing cohorts). The results presented in this work across multiple sites suggest that with further validation deep learning could provide an effective and reliable tool to guide targeted therapy in breast cancer, thus reducing overtreatment among HER2+ patients.Comment: Braman and El Adoui contributed equally to this work. 33 pages, 3 figures in main tex

Prédiction de l’efficacité de la chimiothérapie appliquée au cancer du sein par le traitement d’images et le Deep Learning

Breast cancer is one of the most common diseases in women around the world. This cancer is the leading reason for death in women aged 35 to 70 years old. The growth of cases of breast cancer, as well as a large number of imaging examinations carried out in recent years, provided the development and made it possible to automate several medical imaging techniques. Magnetic resonance imaging (MRI) exams present a great interest to radiologists. Indeed, MRI performs to have a temporal follow-up of the breast tumor thanks to the multiple information and sub-modalities produced by this robust medical imaging modality.In this thesis work, the primary purpose is to help oncologists and radiologists to predict the breast tumor response to chemotherapy. Technically, this could be made by comparing MRI scans before and after the 1st chemotherapy. Such predictions will help to make quick decisions, based on how a breast tumor responds to chemotherapy from the start of therapy.We conducted in-depth research in the literature related to classical imaging approaches, which led us to propose and implement a first technique called Parametric Response Map (PRM). This method is coming off on two primary steps: segmentation and three-dimensional registration of images acquired before and after the 1st chemotherapy. PRM allowed a voxel-by-voxel comparison of the tumor volume. This method produced an easy-to-read color map identifying intra-tumoral regions that responded to treatment (positive response), unresponsive regions (stable), and regions that experienced aggressiveness progression (negative response) indicating the percentage of each tumor's zone.Then, we used deep neural networks for segmenting tumor’s volumes and predicting their response to chemotherapy in an automatic way based on several databases provided by several international institutionsThe promising results of this study show an accuracy value of 89% using the PRM method, and an average accuracy of 93% using Deep Learning across multiple datasets. To our knowledge, these results put themselves above all the presented results in the literature. The standard reference used to validate all the proposed methods is the pathological complete response (pCR) obtained for each patient included in this study.Le cancer du sein est l’une des pathologies les plus fréquentes dans le monde entier. Cette maladie est la première cause de décès chez les femmes de 35 à 70 ans. La croissance des cas de ce type de cancer, ainsi que le grand nombre d’examens d’imagerie effectués dans les dernières années a permis de développer et d’automatiser de nombreuses techniques d’imagerie médicale. Les examens d’imagerie par résonance magnétique (IRM) constituent un grand intérêt pour les radiologues. Cette modalité permet d’avoir un suivi temporel de la tumeur du sein grâce au grand nombre d’informations produites par ses différentes sous-modalités.Dans ce travail, l’objectif principal est d’aider les cancérologues à prédire la réponse tumorale d’un cancer du sein à la chimiothérapie. Techniquement, cela peut s’effectuer par la comparaison des examens IRM d’avant et après la première chimiothérapie. Cela permet d’aider à prendre une décision rapide dès le début de la thérapie.Afin d’atteindre cet objectif, nous avons mené une recherche approfondie dans la littérature liée aux approches classiques d’imagerie. Cette recherche a permis de proposer et implémenter une première méthode appelée la cartographie de la réponse paramétrique (Parametric Response Map : PRM). Cette méthode se base sur deux étapes principales : la segmentation et le recalage tridimensionnel des images acquises avant et après la première chimiothérapie. Ceci permet d’obtenir une comparaison voxel par voxel du volume de la tumeur. Le résultat de la PRM est une cartographie en couleurs facile à lire. Cette carte permet d’identifier trois régions avec leurs pourcentages. Il s’agit des régions intra-tumorales ayant répondu au traitement (réponse positive), des régions n’ayant pas répondu (réponse stable) et des régions qui ont connu une progression d’agressivité (réponse négative).Ensuite, les techniques d’apprentissage profond par réseaux de neurones profonds (Deep Learning) sont proposées pour la segmentation du volume tumoral et la prédiction de la réponse d’un cancer du sein à la chimiothérapie. Afin d’automatiser ce processus, deux bases de données fournies par plusieurs instituts et hôpitaux internationaux ont été utilisées. Les résultats prometteurs de cette étude montrent une valeur de précision de 89% en utilisant la méthode PRM, et une moyenne de précision de 93% pour l’apprentissage profond. De plus, l’explication visuelle des résultats obtenus par le Deep Learning a montré une grande cohérence avec les résultats trouvés par les oncologues. Ces résultats se situent au-dessus de ce qui est présenté dans la littérature. La référence standard utilisée pour valider toutes les méthodes proposées est la réponse pathologique complète obtenue pour chaque patiente

A PRM approach for early prediction of breast cancer response to chemotherapy based on registered MR images

Purpose: This study aims to provide and optimize a performing algorithm for predicting the breast cancer response rate to the first round of chemotherapy using Magnetic Resonance Imaging (MRI). This provides an early recognition of breast tumor reaction to chemotherapy by using the Parametric Response Map (PRM) method. Methods: PRM may predict the breast cancer response to chemotherapy by analyzing voxel-by-voxel temporal intra-tumor changes during one round of chemotherapy. Indeed, the tumor recognizes intra-tumor changes concerning its vascularity, which is an important criterion in the present study. This method is mainly based on spatial image affine registration between the breast tumor MRI volumes, acquired before and after the first cycle of chemotherapy, and region growing segmentation of the tumor volume. To evaluate our method, we used a retrospective study of 40 patients provided by a collaborating institute. Results: PRM allows a color map to be created with the percentages of positive, negative and stable breast tumor response during the first round of chemotherapy, identifying each region with its response rate. We assessed the accuracy of the proposed method using technical and medical validation methods. The technical validation was based on landmarks-based registration and fully manual segmentation. The medical evaluation was based on the accuracy calculation of the standard reference of anatomic pathology. The p-values and the Area Under the Curve (AUC) of the Receiver Operating Characteristics were calculated to evaluate the proposed PRM method. Conclusion: We performed and evaluated the proposed PRM method to study and analyze the behavior of a tumor during the first round of chemotherapy, based on the intra-tumor changes of MR breast tumor images. The AUC obtained for the PRM method is considered as relevant in the early prediction of breast tumor response.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images

Purpose: Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. Computational analysis of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) through deep convolution neural network (CNN) has shown a significant performance to distinguish responders and no responder’s patients. This study intends to present a new deep learning (DL) model predicting the breast cancer response to NAC based on multiple MRI inputs. Methods: A cohort of 723 axial slices extracted from 42 breast cancer patients who underwent NAC therapy was used to train and validate the developed DL model. This dataset was provided by our collaborator institute of radiology in Brussels. Fourteen external cases were used to validate the best obtained model to predict pCR based on pre- and post-chemotherapy DCE-MRI. The model performance was assessed by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and feature map visualization. Results: The developed multi-inputs deep learning architecture was able to predict the pCR to NAC treatment in the validation dataset with an AUC of 0.91 using combined pre- and post-NAC images. The visual results showed that the most important extracted features from non-pCR tumors are in the peripheral region. The proposed method was more productive than the previous ones. Conclusion: Even with a limited training dataset size, the proposed and developed CNN model using DCE-MR images acquired before and after the first chemotherapy was able to classify pCR and non-pCR patients with substantial accuracy. This model could be used hereafter in clinical analysis after its evaluation based on more extra data.SCOPUS: ar.jDecretOANoAutActifinfo:eu-repo/semantics/publishe

L’Internet des Objets met la lumière dans tous ses états

De nombreux objets connectés commencent à faire leur apparition autour de nous à tel point que l’on en vient à se demander s’il est possible de rendre la lumière connectée et intelligente. Nous connaissons tous la domotique et le contrôle de la lumière à partir de dispositifs télécommandés. Avec les technologies émergentes de l’internet des objets, il est aujourd’hui possible d’envisager de nouvelles applications liées à la lumière et à son utilisation dans l’habitat. Grâce à la collaboration entre les objets connectés, des cas d’utilisation comme l’adaptation de la couleur et de l’intensité de la lumière en fonction des émotions des habitants identifiées à l’aide d’une caméra vidéo sont devenus facilement réalisables

Internet of Things: learning and practices. Application to Smart Home

Internet of Things is becoming widely present in our daily life. In fact, more and more devices able to interact together have been recently designed and launched in the market

Effect of Sodium Chloride and Incision on the Chicken Pepsin Coagulant Activity Extracted from Proventriculus, Dried Under Partial Vacuum

&lt;p&gt;Rennet covers only 30% of the world&#39;s cheese production because the availability of calf stomach becomes limited (FAO, 2016). This lack has suggested the search for animal, vegetable or microbial enzyme substitutes. Among the alternative animal enzymes, chicken pepsin. In order to study the chicken pepsin stability over time, chicken proventriculus, whole or incised into four parts or incised into slices, without or with salt addition distributed into six different lots, were dried under partial vacuum (47&deg;C, 800 mbar). The effects of the incision or not, as well as the addition or not of salt, and storage time of dried proventriculus, on the coagulant activity of pepsin extracts (expressed in equivalents Rennet Units) were studied. The six batches pepsin residual activities determined immediately after drying operation expressed the relative yield in the fresh state before storage. They were between 50% for proventriculus cut into four parts without salt addition and 18% for proventriculus with salt addition. After 54 days of storage, the residual activity was relatively distinct for proventriculus cut into four parts without salt addition : 35.5% and for the proventriculus incised into slices with salt addition of 4.7%. Salt seemed to have caused a great loss of activity during salting. In addition, the incision effect combined to salt addition showed a remarkable loss of activity. During the storage period, the pepsin residual coagulant activity showed better stability of partially vacuum-dried proventriculus cut into four parts and unsalted.&lt;/p&gt