ICTLab > Research Projects

Research Projects

Together with most of the partners of this program, we are working on the two following projects.

– ESCAPE is an ANR project that focus on the simulation of urban area evacuation in case of catastrophe. The simulator is based on the gama simulation platform and is based on agent-based modeling of the evacuation process: this allow to explore individually based strategy, including individual knowledge about evacuation plan, emotion during egress and individually based mobility model with several modes. The project focus on three case studies: chemical risk with industry explosion in the center of the city of Rouen (France), flash flood risk at the valley of Authion (France) and Hoa Binh dam break in the region of Hanoi – Red River (Vietnam).

– Gen* is an open source java library that make it possible to generate spatially explicit and socially connected synthetic population using any survey and GIS data. The toolkit is also available as a Gama plugin and can be used through the Kepler workflow management tool. How aim is to provide to computer scientist and none programmers the access to state of the art algorithms to solve synthetic population generation, explicit localization and network generation of synthetic entities. The library is under development but already provide several algorithms for each of this three part: gospl, spll and spin. See https://github.com/ANRGenstar/genstar for further information.

– AgroLD (The Agronomic Linked Data project): Recent advances in high-throughput technologies have resulted in tremendous increase in the amount of data in the agronomic domain. This data explosion in-conjunction with its heterogeneity presents a major challenge in adopting an integrative approach towards research. We are developing AgroLD, a knowledge system that exploits the Semantic Web technology and some of the relevant standard domain ontologies, to integrate information on rice species and in this way facilitating the formulation of new scientific hypotheses. The objective of this effort is to provide the community with a platform for domain specific knowledge, capable of answering complex biological questions. The current phase  covers information on genes, proteins, ontology associations, homology predictions, metabolic pathways, plant traits, and germplasm, on the Arabidopsis and rice species.

– LungCancerCare (A system to support doctors in Lung Cancer Diagnosis and Treatment): Lung cancer is one of the most serious and common types of cancer all over the world, both in number of new patients and in number of fatalities. There were total 1.69 millions of fatalities because of lung cancer in 2015 only (source: World Health Organization). Lung cancer is the most common type of cancer for both genders in Vietnam. According to statistics from Ministry of Health, Vietnam has 25200 new patients in 2017 and expected 30.000 new patents per year in 2020. The goal of this project is to study and develop efficient deep learning models in order to improve the accuracy and computational performance to lung cancer image analysis algorithms. Specific goals include: (1) Building an annotated image dataset for lung cancer analysis with case studies in Vietnamese hospitals; (2) Studying and proposing deep learning models for efficient detection and classification of lung tumors as benign and malignant; (3) Developing a decision support software to assist doctors in lung cancer detection.

iMorph (Detection of geometric morphometric landmarks in 2D insect wing images): The field of morphometrics, or morphometry, is concerned with the analysis of form, which is defined by shape and size, of an object. Shape is defined as the set of geometric properties of an object that are invariant to position, orientation, and scale. In a population of specimens of interest, shape variability with size, i.e. allometry, is also considered. The main goal of morphometrics is to elucidate how shapes vary and their covariance with other variables such as diseases, environmental stresses, or development etc. Morphometrics is very important in biology because it allows quantitative descriptions of organisms, hence facilitating comparative studies by statistical analysis methods. Landmark-based geometric morphometrics uses a set of landmarks to describe shape. Landmark, a two- or three-dimensional point represented by locus coordinates, is described by a tightly defined set of rules that indicates the evolutionary significance for the organism in question. Given these defined rules, it is next necessary to identify the landmarks on each specimen. This task is normally done by an expert, however, it is time-consuming and error-prone. Therefore, in this project, we aim to automate this by processing object images and employing an image recognition & machine learning model to propose landmark candidates.