Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …
information from digital histopathology images. AI is expected to reduce workload for human …
Domain adaptation for medical image analysis: a survey
Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …
from the domain shift problem caused by different distributions between source/reference …
Deep neural network models for computational histopathology: A survey
CL Srinidhi, O Ciga, AL Martel - Medical image analysis, 2021 - Elsevier
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …
underlying mechanisms contributing to disease progression and patient survival outcomes …
Medical image analysis based on deep learning approach
M Puttagunta, S Ravi - Multimedia tools and applications, 2021 - Springer
Medical imaging plays a significant role in different clinical applications such as medical
procedures used for early detection, monitoring, diagnosis, and treatment evaluation of …
procedures used for early detection, monitoring, diagnosis, and treatment evaluation of …
Digital pathology and computational image analysis in nephropathology
The emergence of digital pathology—an image-based environment for the acquisition,
management and interpretation of pathology information supported by computational …
management and interpretation of pathology information supported by computational …
Generative adversarial networks in medical image segmentation: A review
S Xun, D Li, H Zhu, M Chen, J Wang, J Li… - Computers in biology …, 2022 - Elsevier
Abstract Purpose Since Generative Adversarial Network (GAN) was introduced into the field
of deep learning in 2014, it has received extensive attention from academia and industry …
of deep learning in 2014, it has received extensive attention from academia and industry …
[HTML][HTML] Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains
The application of deep learning for automated segmentation (delineation of boundaries) of
histologic primitives (structures) from whole slide images can facilitate the establishment of …
histologic primitives (structures) from whole slide images can facilitate the establishment of …
Deep learning–based segmentation and quantification in experimental kidney histopathology
N Bouteldja, BM Klinkhammer, RD Bülow… - Journal of the …, 2021 - journals.lww.com
Background Nephropathologic analyses provide important outcomes-related data in
experiments with the animal models that are essential for understanding kidney disease …
experiments with the animal models that are essential for understanding kidney disease …
Unpaired stain transfer using pathology-consistent constrained generative adversarial networks
Pathological examination is the gold standard for the diagnosis of cancer. Common
pathological examinations include hematoxylin-eosin (H&E) staining and …
pathological examinations include hematoxylin-eosin (H&E) staining and …
Generative adversarial networks in digital pathology: a survey on trends and future potential
Image analysis in the field of digital pathology has recently gained increased popularity. The
use of high-quality whole-slide scanners enables the fast acquisition of large amounts of …
use of high-quality whole-slide scanners enables the fast acquisition of large amounts of …