The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation
Abstract
We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation. Compared to the commonly used Dice loss, our loss function achieves a better trade off between precision and recall when training on small structures such as lesions. To evaluate our loss function, we improve the attention U-Net model by incorporating an image pyramid to preserve contextual features. We experiment on the BUS 2017 dataset and ISIC 2018 dataset where lesions occupy 4.84% and 21.4% of the images area and improve segmentation accuracy when compared to the standard U-Net by 25.7% and 3.6%, respectively.
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Cites
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Fully convolutional networks for semantic segmentation
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V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2858826 · Cited by 9,556
Attention U-Net: Learning Where to Look for the Pancreas
Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., et al. (2018) · arXiv · doi:10.48550/arxiv.1804.03999 · Cited by 5,458
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Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations
Sudre, C. H., Li, W., Vercauteren, T., Ourselin, S., & Jorge Cardoso, M. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-67558-9_28 · Cited by 2,829
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Automated breast ultrasound lesions detection using convolutional neural networks
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Cited by
Yeung, M., Sala, E., Schönlieb, C.-B., & Rundo, L. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.102026 · Cited by 733
Towards a guideline for evaluation metrics in medical image segmentation
Müller, D., Soto-Rey, I., & Kramer, F. (2022) · BMC Research Notes · doi:10.1186/s13104-022-06096-y · Cited by 668
Loss odyssey in medical image segmentation
Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102035 · Cited by 662
Boundary loss for highly unbalanced segmentation
Kervadec, H., Bouchtiba, J., Desrosiers, C., Granger, E., Dolz, J., & Ben Ayed, I. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101851 · Cited by 532
Sharp U-Net: Depthwise convolutional network for biomedical image segmentation
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Metadata
- Authors
- Abraham, N., & Khan, N. M.
- Venue
- 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
- Publisher
- IEEE
- Published
- 2019-04
- Pages
- 683-687
- OpenAlex
- W2962767316
- Cited by
- 873
- References
- 16
- FWCI
- 31.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Abraham, N., & Khan, N. M. (2019). A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). https://doi.org/10.1109/isbi.2019.8759329
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