Tumor segmentation on whole slide images: training or prompting?
Huaqian Wu Clara Brémond-Martin Kévin Bouaou Cédric Clouchoux
Tumor segmentation stands as a pivotal task in cancer diagnosis. Given the
immense dimensions of whole slide images (WSI) in histology, deep learning
approaches for WSI classification mainly operate at patch-wise or
superpixel-wise level. However, these solutions often struggle to capture
global WSI information and cannot directly generate the binary mask.
Downsampling the WSI and performing semantic segmentation is another possible
approach. While this method offers computational efficiency, it necessitates a
large amount of annotated data since resolution reduction may lead to
information loss. Visual prompting is a novel paradigm that allows the model to
perform new tasks by making subtle modifications to the input space, rather
than adapting the model itself. Such approach has demonstrated promising
results on many computer vision tasks. In this paper, we show the efficacy of
visual prompting in the context of tumor segmentation for three distinct
organs. In comparison to classical methods trained for this specific task, our
findings reveal that, with appropriate prompt examples, visual prompting can
achieve comparable or better performance without extensive fine-tuning.