Tumor segmentation on whole slide images: training or prompting?

arXiv:2402.13932v1 Announce Type: new
Abstract: 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 …

abstract arxiv cancer cancer diagnosis classification cs.cv deep learning diagnosis dimensions global images information pivotal prompting segmentation solutions struggle training type wise