Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology

arXiv:2404.19113v1 Announce Type: cross
Abstract: Given the emergence of deep learning, digital pathology has gained popularity for cancer diagnosis based on histology images. Deep weakly supervised object localization (WSOL) models can be trained to classify histology images according to cancer grade and identify regions of interest (ROIs) for interpretation, using inexpensive global image-class annotations. A WSOL model initially trained on some labeled source image data can be adapted using unlabeled target data in cases of significant domain shifts caused by …

abstract arxiv cancer cancer diagnosis cs.cv cs.lg deep learning diagnosis digital digital pathology domain domain adaptation emergence free identify images interpretation localization object pathology type weakly-supervised