Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labels
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Abstract (arXiv)
A key component towards an improved and fast cancer diagnosis is the development of computer-assisted tools. In this article, we present the solution that won the SegPC-2021 competition for the segmentation of multiple myeloma plasma cells in microscopy images. The labels used in the competition dataset were generated semi-automatically and presented noise. To deal with it, a heavy image augmentation procedure was carried out and predictions from several models were combined using a custom ensemble strategy. State-of-the-art feature extractors and instance segmentation architectures were used, resulting in a mean Intersection-over-Union of 0.9389 on the SegPC-2021 final test set.
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References · 12
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- 2020SCNet: Training Inference Sample Consistency for Instance Segmentationcited by 38
- 2021Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Imagescited by 11
- 2021Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translationcited by 4
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