Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images
This work addresses the challenge of reducing annotation effort for medical professionals in histopathology, though it is incremental as it builds on existing active learning and domain adaptation techniques.
The paper tackles the problem of adapting tissue segmentation models to new histopathological image domains with limited labeled data by combining active learning with domain adaptation, achieving similar F1-scores using only 59% of the training set compared to traditional supervised methods.
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.