Augmented cell-graphs for automated cancer diagnosis
نویسندگان
چکیده
منابع مشابه
Augmented cell-graphs for automated cancer diagnosis
This work reports a novel computational method based on augmented cell-graphs (ACG), which are constructed from low-magnification tissue images for the mathematical diagnosis of brain cancer (malignant glioma). An ACG is a simple, undirected, weighted and complete graph in which a node represents a cell cluster and an edge between a pair of nodes defines a binary relationship between them. Both...
متن کاملSpectral analysis of cell-graphs for automated cancer diagnosis
The cell-graph approach captures the information encoded in tissue samples by capturing the spatial distribution of cells and their cluster formations. In a cellgraph, nodes and edges represent the cell clusters and pairwise relationships between them, respectively. It is shown in [1] that the features of cell-graphs of cancerous tissues are significantly different from those of healthy tissues...
متن کاملEmpowering Multiple Instance Histopathology Cancer Diagnosis by Cell Graphs
We introduce a probabilistic classifier that combines multiple instance learning and relational learning. While multiple instance learning allows automated cancer diagnosis from only image-level annotations, relational learning allows exploiting changes in cell formations due to cancer. Our method extends Gaussian process multiple instance learning with a relational likelihood that brings impro...
متن کاملToward Automated Cancer Diagnosis: An Interactive System for Cell Feature Extraction∗
Oncologists at the University of Wisconsin-Madison have identified nine cell features which allow them to diagnose breast tumors from a fineneedle aspirate. Currently, the physician examines the sample under a microscope and assigns a number in the range 1-10 to each feature, where larger numbers indicate a higher chance of malignancy. These values, viewed as 9-dimensional vectors, are then use...
متن کاملThe cell graphs of cancer
We report a novel, proof-of-concept, computational method that models a type of brain cancer (glioma) only by using the topological properties of its cells in the tissue image. From low-magnification (80x) tissue images of 384 x 384 pixels, we construct the graphs of the cells based on the locations of the cells within the images. We generate such cell graphs of 1000-3000 cells (nodes) with 200...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bioinformatics
سال: 2005
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/bti1100