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Patch-Based Abnormality Maps for Improved Deep Learning-Based Classification of Huntington’s Disease
Book chapter   Peer reviewed

Patch-Based Abnormality Maps for Improved Deep Learning-Based Classification of Huntington’s Disease

Kilian Hett, Rémi Giraud, Hans Johnson, Jane S Paulsen, Jeffrey D Long and Ipek Oguz
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, pp.636-645
Lecture Notes in Computer Science, Springer International Publishing
09/29/2020
DOI: 10.1007/978-3-030-59728-3_62
PMCID: PMC8643359
PMID: 34873594
url
https://hal.science/hal-04405483View
Open Access

Abstract

Deep learning techniques have demonstrated state-of-the-art performances in many medical imaging applications. These methods can efficiently learn specific patterns. An alternative approach to deep learning is patch-based grading methods, which aim to detect local similarities and differences between groups of subjects. This latter approach usually requires less training data compared to deep learning techniques. In this work, we propose two major contributions: first, we combine patch-based and deep learning methods. Second, we propose to extend the patch-based grading method to a new patch-based abnormality metric. Our method enables us to detect localized structural abnormalities in a test image by comparison to a template library consisting of images from a variety of healthy controls. We evaluate our method by comparing classification performance using different sets of features and models. Our experiments show that our novel patch-based abnormality metric increases deep learning performance from 91.3% to 95.8% of accuracy compared to standard deep learning approaches based on the MRI intensity.
 Deep learning Huntington’s disease Patch-based method

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