Primary liver cancer classification from routine tumour biopsy using weakly supervised deep learning

arXiv:2404.04983v1 Announce Type: cross
Abstract: The diagnosis of primary liver cancers (PLCs) can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified PLCs on routine-stained biopsies using a weakly supervised learning method. Weak tumour/non-tumour annotations served as labels for training a Resnet18 neural network, and the network's last convolutional layer was used to extract new tumour tile features. Without knowledge of the precise labels of the malignancies, we then applied an unsupervised clustering algorithm. Our model …

abstract annotations arxiv cancer classification cs.ai cs.cv deep learning diagnosis labels q-bio.to supervised learning training type