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8 décembre 2019

Master 2 internship: Machine learning based classification for identifying different cancer cells in histopathologic images


Catégorie : Stagiaire


Master 2 internship: Machine learning based classification for identifying different cancer cells in histopathologic images

 

Developing histopathologic image analysis algorithms represents a real scientific
challenge. This is mainly due to the lack of representation structure in these
images. More precisely, these images are registered in the form of a pixel matrix in
which no information is provided on the nature of the tissue and its
microenvironment. Additionally, the variation of environmental conditions during the
acquisition process of these images using microscopes will generate a noise that
may affect the analysis results.
One of the promising directions to face the previously mentioned issues is the
integration of artificial intelligence in the developed algorithms. This can be
done using learning techniques to describe and characterize the collected data. In
the histopathologic image analysis field, exploiting this type of techniques has
become an obvious choice for boosting the performance of analysis algorithms [1].
More generally, in the medical and biomedical image analysis field, deep learning
techniques which are mainly based on a convolutional neural network (CNN)
architecture have shown high performance in multiple difficult tasks including
segmentation, classification and retrieval [2,3].
In this context, the main goal of the internship is to develop a machine learning
based classification algorithms to identify different types of cells in a large
histopathological image dataset. The dataset is provided from « L’institut de
pathologie » of CHU and INSERM in Lille.
The funding for the internship is covered by Eurasanté* Innovation and Prevention
award and supported by SANOFI**.
 
  • Required skills
    • Training level: Master 2 or Engineer fifth year
    • Good knowledge on machine learning techniques and image processing Strong
    • capability of coding using Python, R or C/C++ or Matlab is appreciated Good
    • knowledge on biology is a plus
  • Localization ISEN-Lille (Yncréa Hauts de France), 41 boulevard Vauban 59800 Lille, France/ IEMN CNRS laboratory/ digital systems and life sciences team.
 
  • Duration 6 months from 1st February 2019
 
  • Application send your CV and your cover letter to feryal.windal@yncrea.fr and halim.benhabiles@yncrea.fr
 
[1] D. Komura and S. Ishikawa, “Machine Learning Methods for Histopathological Image Analysis,” Computational and Structural Biotechnology Journal. 2018.
 
[2] G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Medical Image Analysis. 2017.
 
[3] H. Benhabiles, K. Hammoudi, F. Windal, M. Melkemi, and A. Cabani, “A Transfer Learning Exploited for Indexing Protein Structures from 3D Point Clouds,” in Processing and Analysis of Biomedical Information (in conjunction with MICCAI 2018), 2018.
 

 
* https://www.eurasante.com/projets_structurants/innovation-prevention
** www.sanofi.fr

 

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