# Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images

arXiv: https://arxiv.org/abs/2303.05225 · HTML: https://mlsanity.com/paper/2303.05225/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 9 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1910.02923/) (2019)
- [Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation](https://mlsanity.com/paper/1706.04737/) (2017)
- [Machine learning methods for histopathological image analysis](https://mlsanity.com/paper/1709.00786/) (2017)
- [Active Learning Using Uncertainty Information](https://mlsanity.com/paper/1702.08540/) (2017)
- [A Closer Look at Domain Shift for Deep Learning in Histopathology](https://mlsanity.com/paper/1909.11575/) (2019)
- [Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis](https://mlsanity.com/paper/1812.07509/) (2018)
- [MedAL: Deep Active Learning Sampling Method for Medical Image Analysis](https://mlsanity.com/paper/1809.09287/) (2018)

## Built on by (most cited first, 2 of 2)

- [Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images](https://mlsanity.com/paper/2405.15264/) (2024)
- [NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning](https://mlsanity.com/paper/2405.15275/) (2024)

## Abstract (arXiv)

Accurate segmentation of tissue in histopathological images can be very beneficial for defining regions of interest (ROI) for streamline of diagnostic and prognostic tasks. Still, adapting to different domains is essential for histopathology image analysis, as the visual characteristics of tissues can vary significantly across datasets. Yet, acquiring sufficient annotated data in the medical domain is cumbersome and time-consuming. The labeling effort can be significantly reduced by leveraging active learning, which enables the selective annotation of the most informative samples. Our proposed method allows for fine-tuning a pre-trained deep neural network using a small set of labeled data from the target domain, while also actively selecting the most informative samples to label next. We demonstrate that our approach performs with significantly fewer labeled samples compared to traditional supervised learning approaches for similar F1-scores, using barely a 59% of the training set. We also investigate the distribution of class balance to establish annotation guidelines.