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Semi–Supervised Learning for Image Modality Classification

Garcia Seco De Herrera, Alba and Markonis, Dimitrios and Joyseeree, Ranveer and Schaer, Roger and Foncubierta-Rodríguez, Antonio and Müller, Henning (2015) Semi–Supervised Learning for Image Modality Classification. In: Multimodal Retrieval in the Medical Domain First International Workshop, MRMD 2015, 2015-03-29 - ?, Vienna, Austria.

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Abstract

Searching for medical image content is a regular task for many physicians, especially in radiology. Retrieval of medical images from the scientific literature can benefit from automatic modality classification to focus the search and filter out non–relevant items. Training datasets are often unevenly distributed regarding the classes resulting sometimes in a less than optimal classification performance. This article proposes a semi–supervised learning approach applied using a k–Nearest Neighbour (k–NN) classifier to exploit unlabelled data and to expand the training set. The algorithmic implementation is described and the method is evaluated on the ImageCLEFmed modality classification benchmark. Results show that this approach achieves an improved performance over supervised k–NN and Random Forest classifiers. Moreover, medical case–based retrieval benefits from the modality filter.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Multimodal Retrieval in the Medical Domain First International Workshop, MRMD 2015, Vienna, Austria, March 29, 2015, Revised Selected Papers. Part of the Lecture Notes in Computer Science book series (LNCS, volume 9059)
Uncontrolled Keywords: case-based retrieval, crowdsourcing, image classification, semi-supervised learning
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 22 Jan 2020 14:06
Last Modified: 22 Jan 2020 15:15
URI: http://repository.essex.ac.uk/id/eprint/22226

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