Semi-Supervised Segmentation of Functional Tissue Units at the Cellular Level
This work addresses segmentation challenges in biomedical imaging for researchers, but it is incremental as it builds on existing techniques without introducing a new paradigm.
The paper tackles the problem of segmenting functional tissue units at the cellular level by developing a method that uses deep learning semantic segmentation with domain adaptation and semi-supervised learning to minimize domain gaps and class imbalances between HPA and HubMAP datasets, achieving results comparable to state-of-the-art.
We present a new method for functional tissue unit segmentation at the cellular level, which utilizes the latest deep learning semantic segmentation approaches together with domain adaptation and semi-supervised learning techniques. This approach allows for minimizing the domain gap, class imbalance, and captures settings influence between HPA and HubMAP datasets. The presented approach achieves comparable with state-of-the-art-result in functional tissue unit segmentation at the cellular level. The source code is available at https://github.com/VSydorskyy/hubmap_2022_htt_solution