# Semi-Supervised Segmentation of Functional Tissue Units at the Cellular Level

Source: [Semi-Supervised Segmentation of Functional Tissue Units at the Cellular Level](<https://www.sota2.com/research/paper/68fa717d1e209131cea51977>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:07:38.985Z

Authors: Volodymyr Sydorskyi, Igor Krashenyi, Denis Sakva, Oleksandr Zarichkovyi

Published: 2023-05-03T14:29:09.000Z

## Abstract

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

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.02148>)

[PDF](<https://arxiv.org/pdf/2305.02148.pdf>)