Deep Learning for Image Analysis

Deep learning has achieved formidable results in the image analysis field in recent years, in many cases exceeding human performance. This success opens paths for new applications, entrepreneurship and research, while making the field very competitive.

This course aims at providing the students with the theoretical and practical basis for understanding and using deep learning for image analysis applications.


Winter 2021 edition: IASD Master

Exam: March 24, 10h - 12h. Classroom V106B, at MINES ParisTech (60 Bd. Saint-Michel).

Lien vers les supports de cours:


Date Time Classroom Lectures Invited speaker
13/01 8h30-11h45 Zoom Machine learning; artificial neural networks; convolutions neural networks I; ethical aspects
20/01 8h30-11h45 Zoom Convolutional neural networks II Vincent Morard
27/01 8h30-11h45 Optimization; fully convolution neural networks II Bruno Figliuzzi
03/02 8h30-11h45 Network introspection and visualization ; object detection Sébastien Lefèvre
10/02 8h30-11h45 DL in practice ; case study C. Breuil, C. Meurée
17/02 8h30-11h45 Reducing supervision Claire-Hélène Demarty
03/03 8h30-11h45 Advanced techniques I ; morphological layers Diego Tuccillo
10/03 8h30-11h45 Advanced techniques II Martin Bauw and/or Valentin Penaud-Polge
17/03 9h-10h30 Exam

Invited speakers

  • Vincent Morard (General Electric) : AI for medical images: an industrial point of view
  • Bruno Figliuzzi (CMM, Mines Paris) : Segmentation d'images de rhéologie par réseaux de neurones convolutionnels
  • Sébastien Lefèvre (IRISA) : Deep Learning in Remote Sensing: Challenges and Results
  • Claire-Hélène Demarty (InterDigital) : Deep Learning for post production in movie industry
  • Camille Breuil et Cédric Meurée (aiVision): L'aide au diagnostic chez aiVision: exemple de la rétinopathie diabétique
  • Diego Tuccillo (Instituto de Astrofisica de Canarias): Deep learning applications in Astronomy
  • Martin Bauw (CMM, Mines Paris): détection d'anomalies
  • Valentin Penaud-Polge (CMM, Mines Paris): couches paramétriques

Propositions de stages

Fall 2020 edition: MINES ParisTech (MP1523/5)

Travaux pratiques

Link to download practical sessions material:

The following assignments will be available from:


First lecture: september 22, 13h45, in classroom L118.

Date Time Classroom
22/09 13h45 L118
29/09 13h35 L109
06/10 13h45 L108
13/10 13h45 V106A
20/10 13h45 L224
27/10 13h45 V106A
04/09 9h V106A
10/09 13h45 V334

Winter 2020 edition: IASD Master

Teaching assistants

  • David Duque
  • Leonardo Gigli
  • Arthur Imbert
  • Tristan Lazard

Fall 2019 Edition: ATHENS MP10

Invited speakers

  • Marc Huertas (Canaries Astrophysics Institute; Observatoire de Paris)
  • Maximilian Jaritz (
  • Olivier Moindrot (Owkin)
  • Bogdan Stanciulescu (CAOR, MINES ParisTech)

Teaching assistants

  • Eric Bazan
  • David Duque
  • Leonardo Gigli
  • Arthur Imbert
  • Tristan Lazard

Fall 2018 Edition: ATHENS MP10

Invited speakers

  • Pierre Fillard (Therapixel)
  • Marc Huertas-Company (Canaries Astrophysics Institute; Observatoire de Paris)
  • Bogdan Stanciulescu (CAOR, MINES ParisTech)
  • Pauline Luc (Facebook AI Research)

Teaching assistants

  • Robin Alais
  • Joseph Boyd
  • Leonardo Gigli
  • Peter Naylor
  • Robin Alais
deep/start.txt · Last modified: 2021/03/02 16:39 by edecenciere
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