Germain Forestier

Université de Haute-Alsace | UHA · IRIMAS

Continuous seismological observations provide valuable insights to deepen our understanding of geological processes and geohazards. We present a systematic analysis of two months of seismological records using an AI-based Self-Supervised Learning (SSL) approach revealing previously undetected seismic events whose physical causes remain unknown but...

Deep learning models have been shown to be a powerful solution for Time Series Classification (TSC). State-of-the-art architectures, while producing promising results on the UCR and the UEA archives , present a high number of trainable parameters. This can lead to long training with high CO2 emission, power consumption and possible increase in the...

In the context of climate change, reducing heatwave and air pollution are major challenges by using nature-based solutions. Urban greening helps to limit heat islands and promote resilience and trees also offer many other advantages in terms of making our cities more sustainable. This study explores the potential of multi-resolution imagery (Pléiad...

In the context of climate change, reducing heatwave and air pollution are major challenges by using nature-based solutions. Urban greening helps to limit heat islands and promote resilience and trees also offer many other advantages in terms of making our cities more sustainable. This study explores the potential of multi-resolution imagery (Pléiad...

Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series analysis where the relevant features must often be abstracted from the raw data but are not known a pri...

Time series data can be found in almost every domain, ranging from the medical field to manufacturing and wireless communication. Generating realistic and useful exemplars and prototypes is a fundamental data analysis task. In this paper, we investigate a novel approach to generating realistic and useful exemplars and prototypes for time series dat...

Adversarial attacks represent a threat to every deep neural network. They are particularly effective if they can perturb a given model while remaining undetectable. They have been initially introduced for image classifiers, and are well studied for this task. For time series, few attacks have yet been proposed. Most that have are adaptations of att...

Time series averages are one key input to temporal data mining techniques such as classification, clustering, forecasting, etc. In practice, the optimality of estimated averages often impacts the performance of such temporal data mining techniques. Practically, an estimated average is presumed to be optimal if it minimizes the discrepancy between i...

Colorectal cancer is responsible of the death of hundred of thousands of people worldwide each year. The histopathological features of the tumor are generally identified from the analysis of tissue taken from a biopsy providing information for selecting the adequate treatment. With the advent of digital pathology, slides of tissue are increasingl...

The measurement of progress using benchmarks evaluations is ubiquitous in computer science and machine learning. However, common approaches to analyzing and presenting the results of benchmark comparisons of multiple algorithms over multiple datasets, such as the critical difference diagram introduced by Dem\v{s}ar (2006), have important shortcomin...

Land use and land cover and Urban Fabric (UF) mapping are very useful for urban modeling and simulation (growth, pollution, noise, micro-climate, mobility) in a context of global change. In recent years, due to the increase of Earth Observation data researchers built and shared datasets to the machine learning scientific community to apply and test...

Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series analysis where the relevant features must often be abstracted from the raw data but are not known a pri...

Deep Learning models for time series classification are benchmarked on the UCR Archive. This archive contains 128 datasets. Unfortunately only 5 datasets contain more than 1000 training samples. For most deep learning models, this lead to over-fitting. One way to address this issue and improve the generalization of the models is data augmentation....

In the context of global change, up-to-date land use land cover (LULC) maps is a major challenge to assess pressures on natural areas. These maps also allow us to assess the evolution of land cover and to quantify changes over time (such as urban sprawl), which is essential for having a precise understanding of a given territory. Few studies have c...

An Augmented Reality Microscope (ARM) displays additional information about the tissues being analyzed by a pathologist. The image analysis methods used by these microscopes must be robust to changes in magnification level in order to follow the displacements in the glass slides. In this paper, we propose to take advantage of features present in so...

Recently, Artificial Intelligence namely Deep Learning methods have revolutionized a wide range of domains and applications. Besides, Digital Pathology has so far played a major role in the diagnosis and the prognosis of tumors. However, the characteristics of the Whole Slide Images namely the gigapixel size, high resolution and the shortage of ric...

In today’s data-driven world, time series forecasting is one of the intensively investigated temporal data mining technique. In practice, there are a range of forecasting techniques that are proven to be efficient at capturing different aspect of an input. For instance, classic linear forecasting models such as Seasonal Auto-Regressive Integrated M...

This paper presents MultiSenGE that is a new large scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. MultiSenGE contains 8,157 patches of 256 × 256 pixels for the Sentinel-2 L2A , Sentinel-1 GRD images in VV-VH polarization and a Regional large scale Land Us...

Adversarial attacks represent a threat to every deep neural network. They are particularly effective if they can perturb a given model while remaining undetectable. They have been initially introduced for image classifiers, and are well studied for this task. For time series, few attacks have yet been proposed. Most that have are adaptations of att...

Urban areas are increasing since several years as a result of development of built-up areas, network infrastructure, industrial areas or other built-up areas. This urban sprawl has a considerable impact on natural areas by changing the functioning of ecosystems. Mapping and monitoring Urban Fabrics (UF) is therefore relevant for urban planning and...

Time series are ubiquitous in data mining applications. Similar to other types of data, annotations can be challenging to acquire, thus preventing from training time series classification models. In this context, clustering methods can be an appropriate alternative as they create homogeneous groups allowing a better analysis of the data structure....

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing num...

Nowadays, digital pathology plays a major role in the diagnosis and prognosis of tumours. Unfortunately, existing methods remain limited when faced with the high resolution and size of Whole Slide Images (WSIs) coupled with the lack of richly annotated datasets. Regarding the ability of the Deep Learning (DL) methods to cope with the large scale ap...

Objective: This article presents an automatic image processing framework to extract quantitative high-level information describing the micro-environment of glomeruli in consecutive whole slide images (WSIs) processed with different staining modalities of patients with chronic kidney rejection after kidney transplantation. Methods: This four-step fr...

Anatomical Pathology dates back to the nineteenth century when Rudolf Virchow introduced his concept of cellular pathology and when the technical improvements of light microscopy enabled wide-spread use of structural criteria to define diseases. Since then, the quality of optical instruments has been constantly evolving. However the central element...

When overpopulated cities face frequent crowded events like strikes, demonstrations, parades or other sorts of people gatherings, they are confronted to multiple security issues. To mitigate these issues, security forces are often involved to monitor the gatherings and to ensure the security of their participants. However, when access to technology...

The estimation of an optimal time series average has been studied for over three decades. The process is mainly challenging due to temporal distortion. Previous approaches mostly addressed this challenge by using alignment algorithms such as Dynamic Time Warping (DTW). However, the quadratic computational complexity of DTW and its inability to alig...

This paper brings deep learning at the forefront of research into time series classification (TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of time series. The last few decades of work in this area have led to significant progress in the accuracy of classifiers, with the state of the art now represented by...

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical data science is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of...

Objective: This article presents an automatic image processing framework to extract quantitative high-level information describing the micro-environment of glomeruli in consecutive whole slide images (WSIs) processed with different staining modalities of patients with chronic kidney rejection after kidney transplantation. Methods: This three step f...

Staying aware of new approaches emerging within specific areas can be challenging for researchers who have to follow many feeds such as journals articles, authors’ papers, and other basic keyword-based matching algorithms. Hence, this paper proposes an information retrieval process for scientific articles aiming to suggest semantically related arti...

Landscape reconstruction is crucial to measure the effects of climate change or past land use on current biodiversity. In particular, retracing past phenological changes can serve as a basis for explaining current patterns of plant communities and predict the future extinction of species. Old spatial data are currently used to reconstruct vegetatio...

The field of digital pathology emerged with the introduction of whole slide imaging scanners and lead to the development of new tools for analyzing histopathological slides. The availability of digital representation of the slides has motivated the development of artificial intelligence methods to automatically identify microscopic structures in or...

Clustering is anGançarski, Pierre unsupervised processDao, Thi-Bich-Hanh which aims to discover regularities and underlying structures in data. Constrained clustering extends clusteringCrémilleux, Bruno in such a way that expert knowledge can be integrated through the use of user constraints. These guideForestier, Germain the clustering process tow...

In order to understand web-based application user behavior, web usage mining applies unsupervised learning techniques to discover hidden patterns from web data that captures user browsing on web sites. For this purpose, web session clustering has been among the most popular approaches to group users with similar browsing patterns that reflect their...

Our aim is to complement observer-dependent approaches of immune cell evaluation in microscopy images with reproducible measures for spatial composition of lymphocytic infiltrates. Analyzing such patterns of inflammation is becoming increasingly important for therapeutic decisions, for example in transplantation medicine or cancer immunology. We de...

Clinical reasoning is at the heart of physicians' competence, as it allows them to make diagnoses. However, diagnostic errors are common, due to the existence of reasoning biases. Artificial intelligence is undergoing unprecedented development in this context. It is increasingly seen as a solution to improve the diagnostic performance of physicians...

Résumé : L’anatomopathologie remonte au 19ème siècle. Depuis, le matériel n’a cessé d’évoluer, mais l’oeil expert du pathologiste reste incontournable. De même, le processus complet (préparation du tissu et des lames, coloration, contrôle qualité) consiste encore en beaucoup d’étapes manuelles. Grâce à l’avènement récent de scanners numériques à bo...

Time series classification (TSC) is the area of machine learning interested in learning how to assign labels to time series. The last few decades of work in this area have led to significant progress in the accuracy of classifiers, with the state of the art now represented by the HIVE-COTE algorithm. While extremely accurate, HIVE-COTE is infeasibl...

Purpose: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented need to provide an accurate, objective and automatic evaluation of trainees' surgical skills in...

Purpose Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented need to provide an accurate, objective and automatic evaluation of trainees’ surgical skills in o...

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful app...

Over the past one hundred years, the classic teaching methodology of “see one, do one, teach one” has governed the surgical education systems worldwide. With the advent of Operation Room 2.0, recording video, kinematic and many other types of data during the surgery became an easy task, thus allowing artificial intelligence systems to be deployed a...

Over the past one hundred years, the classic teaching methodology of "see one, do one, teach one" has governed the surgical education systems worldwide. With the advent of Operation Room 2.0, recording video, kinematic and many other types of data during the surgery became an easy task, thus allowing artificial intelligence systems to be deployed a...

Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety. With the recent success of deep neural networks in various domains such as computer vision and natural language processing, researchers started adopting these techniques for...

Deep neural networks have revolutionized many fields such as computer vision and natural language processing. Inspired by this recent success, deep learning started to show promising results for Time Series Classification (TSC). However, neural networks are still behind the state-of-the-art TSC algorithms, that are currently composed of ensembles o...

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

Crowdsourcing in pathology has been performed on tasks that are assumed to be manageable by nonexperts. Demand remains high for annotations of more complex elements in digital microscopic images, such as anatomical structures. Therefore, this work investigates conditions to enable crowdsourced annotations of high-level image objects, a complex task...

Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network's weights) to a second network to be trained on a target dataset. This idea has been shown to improve deep neural network's generalization capabilities in many computer vision tasks...

Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network's weights) to a second network to be trained on a target dataset. This idea has been shown to improve deep neural network's generalization capabilities in many computer vision tasks...