Dissertation

Apprentissage de représentations pour la classification d'images biomédicales

Thong, William

2015

Overview

The growing accessibility of medical imaging provides new clinical applications for patient care. New clinically relevant features can now be discovered to understand, describe and represent a disease. Traditional algorithms based on hand-engineered features usually fail in biomedical applications because of their lack of ability to capture the high variability in the data. Representation learning, often called deep learning, tackles this challenge by learning multiple levels of representation. The hypothesis of this master’s thesis is that representation learning for biomedical image classification will yield additional information for the physician in his decision-making process. Therefore, the main objective is to assess the feasibility of representation learning for two different biomedical applications in order to learn clinically relevant structures within the data. First, a non-supervised learning algorithm extracts discriminant features to describe spine deformities that require a surgical intervention in patients with adolescent idiopathic scoliosis. The sub-objective is to propose an alternative to existing scoliosis classifications that only characterize spine deformities in 2D whereas a scoliotic is often deformed in 3D. 915 spine reconstructions from 663 patients were collected. Stacked auto-encoders learn a hidden representation of these reconstructions. This low-dimensional representation disentangles the main factors of variation in the geometrical appearance of spinal deformities. Sub-groups are clustered with the k-means++ algorithm. Eleven statistically significant sub-groups are extracted to explain how the different deformations of a scoliotic spine are distributed. Secondly, a supervised learning algorithm extracts discriminant features in medical images. The sub-objective is to classify every voxel in the image in order to produce kidney segmentations. 79 contrast-enhanced CT scans from 63 patients with renal complications were collected. A convolutional network is trained on a patch-based training scheme. Simple modifications to the architecture of the network, without modifying the parameters, compute the kidney segmentations on the whole image in a small amount of time. Results show high scores on the metrics used to assess the segmentations. Dice scores are 94.35% for the left kidney and 93.07% for the right kidney. The results show new perspectives for the diseases addressed in this master’s thesis. Representation learning algorithms exhibit new opportunities for an application in other biomedical tasks as long as enough observations are available.

• Publisher: ProQuest Dissertations & Theses
• Subject: Biomedical engineering / Medical imaging
• ISBN: 1369488157, 9781369488159