Abstract
We introduce a deep neural network that can be used to localize and detect a region of interest (ROI) in an image. We show how this network helped us extract ROIs when working on two separate problems: a whale recognition problem and a heart volume estimation problem. In the former problem, we used this network to localize the head of the whale while in the later we used it to localize the heart left ventricle from MRI images. Most localization networks regress a bounding box around the region of interest. Unlike these architecture, we treat the problem as a classification problem where each pixel in the image is a separate class. The network is trained on images along with masks which indicate where the object is in the image. We treat the problem as a multi-class classification. Therefore, the last layer has a softmax activation. Furthermore, during training, the mutli-class log loss is minimized just like any classification task.
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Acknowledgements
This research is partially funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). This support is greatly appreciated. We would also like to thank kaggle, the National Oceanic Atmospheric Administration Fisheries for providing the whale data set. We would also like to thank Booz Allen Hamilton, and the National Heart, Lung, and Blood Institute (NHLBI) for providing the MRI images.
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Kabani, A., El-Sakka, M.R. (2016). Object Detection and Localization Using Deep Convolutional Networks with Softmax Activation and Multi-class Log Loss. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_41
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DOI: https://doi.org/10.1007/978-3-319-41501-7_41
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