Abstract
Different works have shown that the combination of multiple loss functions is beneficial when training deep neural networks for a variety of prediction tasks. Generally, such multi-loss approaches are implemented via a weighted multi-loss objective function in which each term encodes a different desired inference criterion. The importance of each term is often set using empirically tuned hyper-parameters. In this work, we analyze the importance of the relative weighting between the different terms of a multi-loss function and propose to leverage the model’s uncertainty with respect to each loss as an automatically learned weighting parameter. We consider the application of colon gland analysis from histopathology images for which various multi-loss functions have been proposed. We show improvements in classification and segmentation accuracy when using the proposed uncertainty driven multi-loss function.
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BenTaieb, A., Hamarneh, G. (2017). Uncertainty Driven Multi-loss Fully Convolutional Networks for Histopathology. In: Cardoso, M., et al. Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. LABELS STENT CVII 2017 2017 2017. Lecture Notes in Computer Science(), vol 10552. Springer, Cham. https://doi.org/10.1007/978-3-319-67534-3_17
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DOI: https://doi.org/10.1007/978-3-319-67534-3_17
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