![]() ImageCLEF is an ongoing initiative that promotes the evaluation of technologies for annotation, indexing and retrieval for providing information access to databases of images in various usage scenarios and domains. This paper presents an overview of the ImageCLEF 2015 evaluation campaign, an event that was organized as part of the CLEF labs 2015. To support our analysis we cross-validate model performance to reduce bias and generalization errors and perform statistical analyses to assess performance differences. In this study, we visualize the learned weights and salient network activations in a CNN based Deep Learning (DL) model to determine the image characteristics that lend themselves for improved classification with a goal of developing informed clinical question-answering systems. However, it is poorly understood how these algorithms discriminate the modalities and if there are implicit opportunities for improving visual information access applications in computational biomedicine. Researchers use novel machine learning (ML) tools to classify the medical imaging modalities. This lack of transparency is a drawback since poorly understood model behavior could adversely impact subsequent decision-making. However, these models are perceived as black boxes since there is a lack of understanding of their learned behavior from the underlying task of interest. Convolutional neural network (CNN) has become the architecture of choice for visual recognition tasks. ![]()
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