Methods for nuclei detection, segmentation, and classification in digital histopathology: a review—current status and future potential
Digital pathology represents one of the major evolutions in modern medicine. Pathological
examinations constitute the gold standard in many medical protocols, and also play a critical …
examinations constitute the gold standard in many medical protocols, and also play a critical …
[PDF][PDF] A review on graph based segmentation
KS Camilus, VK Govindan - … Journal of Image, Graphics and Signal …, 2012 - mecs-press.org
Image segmentation plays a crucial role in effective understanding of digital images. Past
few decades saw hundreds of research contributions in this field. However, the research on …
few decades saw hundreds of research contributions in this field. However, the research on …
Weakly supervised histopathology cancer image segmentation and classification
Labeling a histopathology image as having cancerous regions or not is a critical task in
cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster …
cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster …
Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection
Different types of breast cancer are affecting lives of women across the world. Common
types include Ductal carcinoma in situ (DCIS), Invasive ductal carcinoma (IDC), Tubular …
types include Ductal carcinoma in situ (DCIS), Invasive ductal carcinoma (IDC), Tubular …
Integrating deep convolutional neural networks with marker-controlled watershed for overlapping nuclei segmentation in histopathology images
Nuclei segmentation in histopathology images plays a crucial role in the morphological
quantitative analysis of tissue structure and has become a hot research topic. Though …
quantitative analysis of tissue structure and has become a hot research topic. Though …
Segmentation of cervical cell nuclei in high-resolution microscopic images: a new algorithm and a web-based software framework
In order to automate cervical cancer screening tests, one of the most important and
longstanding challenges is the segmentation of cell nuclei in the stained specimens. Though …
longstanding challenges is the segmentation of cell nuclei in the stained specimens. Though …
Analysis of histopathology images: From traditional machine learning to deep learning
Digitizing pathology is a current trend that makes large amounts of visual data available for
automatic analysis. It allows to visualize and interpret pathologic cell and tissue samples in …
automatic analysis. It allows to visualize and interpret pathologic cell and tissue samples in …
Multi-resolution graph-based analysis of histopathological whole slide images: Application to mitotic cell extraction and visualization
In this paper, we present a graph-based multi-resolution approach for mitosis extraction in
breast cancer histological whole slide images. The proposed segmentation uses a multi …
breast cancer histological whole slide images. The proposed segmentation uses a multi …
Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features
Phase-contrast microscopy is one of the most common and convenient imaging modalities
to observe long-term multi-cellular processes, which generates images by the interference of …
to observe long-term multi-cellular processes, which generates images by the interference of …
Transfer learning with pre-trained deep convolutional neural networks for serous cell classification
Serous effusion is a condition of excess accumulation of fluids in serous cavities due to
different underlying pathological conditions. The basis of cytopathological assessment of …
different underlying pathological conditions. The basis of cytopathological assessment of …