An analysis of loss functions for heavily imbalanced lesion segmentation

Mariano Cabezas*, Yago Diez

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Heavily imbalanced datasets are common in lesion segmentation. Specifically, the lesions usually comprise less than 5% of the whole image volume when dealing with brain MRI. A common solution when training with a limited dataset is the use of specific loss functions that rebalance the effect of background and foreground voxels. These approaches are usually evaluated running a single cross-validation split without taking into account other possible random aspects that might affect the true improvement of the final metric (i.e., random weight initialisation or random shuffling). Furthermore, the evolution of the effect of the loss on the heavily imbalanced class is usually not analysed during the training phase. In this work, we present an analysis of different common loss metrics during training on public datasets dealing with brain lesion segmentation in heavy imbalanced datasets. In order to limit the effect of hyperparameter tuning and architecture, we chose a 3D Unet architecture due to its ability to provide good performance on different segmentation applications. We evaluated this framework on two public datasets and we observed that weighted losses have a similar performance on average, even though heavily weighting the gradient of the foreground class gives better performance in terms of true positive segmentation.

Original languageEnglish
Article number1981
Pages (from-to)1-21
Number of pages21
JournalSensors
Volume24
Issue number6
DOIs
Publication statusPublished - Mar 2024
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2024. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • brain
  • imbalanced dataset
  • lesion segmentation
  • loss functions
  • magnetic resonance imaging
  • medical imaging sensors

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