Traditional Feature Engineering and Deep Learning Approaches at Medical Classification Task of ImageCLEF 2016
نویسندگان
چکیده
This paper describes the modeling approaches used for the Subfigure Classification subtask at ImageCLEF 2016 by the FHDO Biomedical Computer Science Group (BCSG). Besides traditional feature engineering, modern Deep Convolutional Neural Networks (DCNN) were used, trained from scratch and using a transfer learning scenario. In addition Bag-of-Visual-Words (BoVW) were computed in Opponent color space, since some classes in this subtask can be distinguished by color. To remove unimportant visual words the Information Gain is used for Feature Selection. Overall BCSG achieved top performance for all three types of features: textual, visual and mixed.
منابع مشابه
Overview of the ImageCLEF 2016 Medical Task
ImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CLEF). ImageCLEF has historically focused on the multimodal and language–independent retrieval of images. Many tasks are related to image classification and the annotation of image data as well. The medical task has focused more on image retrieval in the beginning and then retrieval and classification task...
متن کاملSentiment Analysis and Deep Learning: A Survey
Deep learning has an edge over the traditional machine learning algorithms, like SVM and Naı̈ve Bayes, for sentiment analysis because of its potential to overcome the challenges faced by sentiment analysis and handle the diversities involved, without the expensive demand for manual feature engineering. Deep learning models promise one thing given sufficient amount of data and sufficient amount o...
متن کاملNovaSearch at ImageCLEFmed 2016 Subfigure Classification Task
This paper describes the NovaSearch team participation in the ImageCLEF 2016 Medical Task in the subfigure classification subtask. Deep learning techniques have proved to be very effective in automatic representation learning and classification tasks with general data. More specifically, convolutional neural networks (CNNs) have surpassed humanlevel performance in the ImageNET classification ta...
متن کاملFCSE at Medical Tasks of ImageCLEF 2013
This paper presents the details of the participation of FCSE (Faculty of Computer Science and Engineering) research team in ImageCLEF 2013 medical tasks (modality classification, ad-hoc image retrieval and case-based retrieval). For the modality classification task we used SIFT descriptors and tf − idf weights of the surrounding text (image caption and paper title) as features. SVMs with χ kern...
متن کاملA novel method based on a combination of deep learning algorithm and fuzzy intelligent functions in order to classification of power quality disturbances in power systems
Automatic classification of power quality disturbances is the foundation to deal with power quality problem. From the traditional point of view, the identification process of power quality disturbances should be divided into three independent stages: signal analysis, feature selection and classification. However, there are some inherent defects in signal analysis and the procedure of manual fe...
متن کامل