Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in
each domain is driven by a growing body of annotated data, increased computational …

Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …

Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural
network denoising SRS photos: hyperspectral resolution enhancement and denoising one …

Nuclei segmentation is a fundamental but challenging task in the quantitative analysis of
histopathology images. Although fully-supervised deep learning-based methods have made …

Reporting biomarkers assessed by routine immunohistochemical (IHC) staining of tissue is
broadly used in diagnostic pathology laboratories for patient care. So far, however, clinical …

Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant
accumulation of immature lymphocytes in the blood or bone marrow. Effective treatment of …

Recent advances in unsupervised domain adaptation (UDA) techniques have witnessed
great success in cross-domain computer vision tasks, enhancing the generalization ability of …

Recently deep neural networks, which require a large amount of annotated samples, have
been widely applied in nuclei instance segmentation of H&E stained pathology images …

Image segmentation for video analysis plays an essential role in different research fields
such as smart city, healthcare, computer vision and geoscience, and remote sensing …

In this work, we present an unsupervised domain adaptation (UDA) method, named
Panoptic Domain Adaptive Mask R-CNN (PDAM), for unsupervised instance segmentation …