Abstract
Although numerous improvements have been made in the field of image segmentation using convolutional neural networks, the majority of these improvements rely on training with larger datasets, model architecture modifications, novel loss functions, and better optimizers. In this paper, we propose a new segmentation performance boosting paradigm that relies on optimally modifying the network’s input instead of the network itself. In particular, we leverage the gradients of a trained segmentation network with respect to the input to transfer it to a space where the segmentation accuracy improves. We test the proposed method on three publicly available medical image segmentation datasets: the ISIC 2017 Skin Lesion Segmentation dataset, the Shenzhen Chest X-Ray dataset, and the CVC-ColonDB dataset, for which our method achieves improvements of 5.8%, 0.5%, and 4.8% in the average Dice scores, respectively.
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Acknowledgement
Partial funding for this project is provided by the Natural Sciences and Engineering Research Council of Canada (NSERC). The authors are grateful to the NVIDIA Corporation for donating Titan X GPUs and to Compute Canada for HPC resources used in this research.
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Taghanaki, S.A., Abhishek, K., Hamarneh, G. (2019). Improved Inference via Deep Input Transfer. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_91
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DOI: https://doi.org/10.1007/978-3-030-32226-7_91
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