Master Thesis Online
Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition
Mastrodicasa, Simon 
Promotor(s) :
Phillips, Christophe
Date of defense : 25-Jun-2018/26-Jun-2018 • Permalink : http://hdl.handle.net/2268.2/4673
Details
| Title : | Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition |
| Translated title : | [fr] Deep Learning Multi-Cas Multi-Etapes pour la Reconnaissance en Imagerie Medicale |
| Author : | Mastrodicasa, Simon
|
| Date of defense : | 25-Jun-2018/26-Jun-2018 |
| Advisor(s) : | Phillips, Christophe
|
| Committee's member(s) : | Zhang, Gary
Geurts, Pierre
Louppe, Gilles
|
| Language : | English |
| Number of pages : | 65 |
| Keywords : | [en] Machine learning [en] MRI [en] Deep Learning [en] Multi-Instance |
| Discipline(s) : | Engineering, computing & technology > Computer science |
| Research unit : | University College of London |
| Target public : | Researchers |
| Complementary URL : | https: //github.com/Mastrodicasa/Master_Thesis.git |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Degree: | Master en ingénieur civil biomédical, à finalité spécialisée |
| Faculty: | Master thesis of the Faculté des Sciences appliquées |
Abstract
[en] Given a 2D transversal slice, identify which body section it belongs to, which is a
classification problem.
The twist is, instead of having a database with segmented body parts (example:
in this image, the heart is placed there, which is the discriminative feature of the
"cardiac" body section), image level label (example:this image belongs to the "car-
diac" body section) are used to train the algorithm, reducing greatly the annotation
time done by a specialist.
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Multi_Stage_Medical_Imaging_Recognition_Mastrodicasa_Simon.pdf
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The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.