Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing …
S Banerji, S Mitra - Wiley Interdisciplinary Reviews: Data …, 2022 - Wiley Online Library
Histopathology is diagnosis based on visual examination of tissue sections under a microscope. With the growing number of digitally scanned tissue slide images, computer …
N Hatipoglu, G Bilgin - Medical & biological engineering & computing, 2017 - Springer
In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief …
Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very …
Digital histopathological images, high‐resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state …
H Jung, B Lodhi, J Kang - BMC Biomedical Engineering, 2019 - Springer
Background Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the images need to be …
Deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features …
Y Jiang, M Yang, S Wang, X Li… - Cancer communications, 2020 - Wiley Online Library
The development of digital pathology and progression of state‐of‐the‐art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI) …
Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to disease progression and patient survival outcomes …