Unsupervised Prostate Cancer Histopathology Image Segmentation via Meta-Learning
Nikolai Fetisov, Lawrence Hall, Dmitry B. Goldgof, Matthew B. Schabath · 2023
We propose a novel unsupervised meta-learning based segmentation algorithm for histopathology images. The proposed algorithm does not require any kind of patch-level annotations and relies solely on image labels, corresponding to any classification task, and direct feedback from a classifier. Furthermore, instead of simply segmenting histopathology images into different types of tissue, our algorithm determines the relative importance of each tissue region. After thresholding, the produced segmentations can also be used as regions of interest for various machine learning based diagnosis systems. We have tested our approach on Prostate cANcer graDe Assessment (PANDA) dataset and obtained 0.79 AUC, when testing the segmentation performance at patch-level, and 0.432 Dice coefficient, when testing precise segmentation, which is comparable to 0.446, described in related work which performed a supervised segmentation with U-Net. Note that no pixel level annotations were used.