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Postdoctoral position in artificial intelligence for diabetic retinopathy management

9 Septembre 2021


Catégorie : Post-doctorant


The Laboratory of medical information processing (LaTIM – UMR 1101 INSERM) is opening a 3-year postdoc position in artificial intelligence for diabetic retinopathy management, within the framework of the ANR RHU EviRed project (Intelligent Evaluation of Diabetic Retinopathy).

 

Context

EviRed Project

Diabetic retinopathy (DR), an ocular complication of diabetes, is a leading cause of blindness in developed countries. An important obstacle to fight DR is the use of a classification based on an old imaging technique: color fundus photography (CFP). This classification is insufficient to finely predict the future evolution: in ~50% of cases, ophthalmologists over or under-estimate the advent of complications. New available imaging techniques are more powerful in this context: ultra-wide-field color fundus photography (UWF-CFP) gives useful 2-D information on the periphery of the retina, not seen on standard CFP. Structural optical coherence tomography (OCT), which produces few microns resolution cross sectional 3-D imaging, is the reference for the diagnosis of diabetic macular edema, one complication of DR. It has been enriched with OCT angiography (OCTA), which can show the vasculature of the retina non-invasively. However, these new imaging modalities produce an expanding amount of data which requires high human expertise. Any clinical score based on them will be complex and challenging for most ophthalmologists. Therefore, the purpose of the EviRed project is to replace the current classification with an AI-based expert system integrating multi-modal data to propose diagnosis and prediction.

The EviRed project groups together the LaTIM laboratory, Paris Hospitals (AP-HP) and three companies (ADCIS, Evolucare Technologies and ZEISS).

LaTIM Laboratory

The postdoc will be hosted by LaTIM, in Brest, France, which leads AI development in the EviRed project. Born from the complementarity between Health and Communication sciences, the LaTIM ("laboratoire de traitement de l’information médicale" for laboratory of medical information processing) develops multidisciplinary research driven by members from University of Western Brittany (UBO), IMT Atlantique, INSERM and Brest University Hospitals (CHRU de Brest). Information is at the heart of the research project of the unit; being by nature multimodal, complex, heterogeneous, shared and distributed, it is integrated by researchers into methodological solutions for improving medical care. Benefiting from a unit within the CHRU, the UMR (joint research unit) has (in addition to access to its own platforms) a privileged access to hospital technical platforms, as well as to all clinical data and patients, in a strong dynamic of translational research.

Research Topic

One challenge in the EviRed project is to combine visual information from various imaging modalities (UWF-CFP, OCT, WF-OCTA), as well as contextual information collected in ophthalmology departments (e.g. visual acuity) and in diabetology departments (e.g. diabetes stability). We will have to develop neural architectures able to jointly process these multi-modal sources of information in order to assess DR severity and progression.

Another challenge is to analyze follow-up examinations in order to improve DR severity and progression assessment. The goal is to take advantage of past examinations in addition to the current one. We will have to develop time series analysis tools able to capture and extrapolate evolution between consecutive examinations. Visualization techniques will be employed for the extraction and analysis of spatio-temporal predictive patterns in follow-up examinations, to elucidate the changes and early-warning signs that should be looked for in examination records.

Two PhD students have been recruited at LaTIM for the EviRed project: one for each of these two challenges (multi-modal fusion, longitudinal analysis). The postdoc is expected to help supervise these students, coordinate their research works and integrate their contributions into a unified AI solution for the EviRed project. In parallel, he or she will investigate additional AI tools on his or her own to enrich this unified solution. This will imply continually monitoring the state of the art. Finally, the result of this research will have to be disseminated through patent applications and publications.

Recent publications from the team and the consortium

  • Quellec G, Al Hajj H, Lamard M, Conze P-H, Massin P, Cochener B. ExplAIn: Explanatory artificial intelligence for diabetic retinopathy diagnosis. Med Image Anal. 2021 Aug;72:102118.
  • Yan Y, Conze P-H, Lamard M, Quellec G, Cochener B, Coatrieux G. Towards improved breast mass detection using dual-view mammogram matching. Med Image Anal. 2021 Jul;71:102083.
  • Yan Y, Conze P-H, Quellec G, Massin P, Lamard M, Coatrieux G, Cochener B. Longitudinal detection of diabetic retinopathy early severity grade changes using deep learning. MICCAI workshop on Ophtalmic Medical Image Analysis, 2021.
  • Quellec G, Lamard M, Conze P-H, Massin P, Cochener B. Automatic detection of rare pathologies in fundus photographs using few-shot learning. Medical Image Analysis. 2020 Apr;61:101660.
  • Al Hajj H, Lamard M, Conze P-H, Cochener B, Quellec G. Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks. Med Image Anal. 2018 Jul;47:203–18.
  • Bonnin S, Krivosic V, Cognat E, Tadayoni R. Visibility of blood flow on optical coherence tomography angiography in a case of branch retinal artery occlusion. J Ophthalmic Vis Res. 2018;13(1):75–7.

Required Skills

  • PhD in AI and/or image processing
  • Python programming
  • AI libraries (Tensorflow, Keras, Pytorch, etc.)
  • Fluent English for reading/writing scientific articles
  • Organizational skills

Details

Starting date

Between November 2021 and January 2022.

Duration

3 years.

Application

Please send a detailed resume with a list of publications, a cover letter and references to: