Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labels
This work addresses the need for accurate computer-assisted cancer diagnosis tools, though it is incremental as it builds on existing methods for a competition dataset.
The paper tackled the problem of segmenting multiple myeloma plasma cells in microscopy images with noisy labels, achieving a mean Intersection-over-Union of 0.9389 on the SegPC-2021 test set.
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.