Artificial Neural Networks: A Tutorial
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Bayesian Networks in Ovarian Cancer Diagnosis: Potentials and Limitations
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
Support vector machines for classification of histopathological images of brain tumour astrocytomas
ICCMSE '03 Proceedings of the international conference on Computational methods in sciences and engineering
Bioinformatics
IEEE Transactions on Information Technology in Biomedicine
Fractal analysis in the detection of colonic cancer images
IEEE Transactions on Information Technology in Biomedicine
Lung cancer cell identification based on artificial neural network ensembles
Artificial Intelligence in Medicine
Effective graph classification based on topological and label attributes
Statistical Analysis and Data Mining
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This work presents a graph-based representation (a.k.a., cell-graph) of histopathological images for automated cancer diagnosis by probabilistically assigning a link between a pair of cells (or cell clusters). Since the node set of a cell-graph can include a cluster of cells as well as individual ones, it enables working with low-cost, low-magnification photomicrographs. The contributions of this work are twofold. First, it is shown that without establishing a pairwise spatial relation between the cells (i.e., the edges of a cell-graph), neither the spatial distribution of the cells nor the texture analysis of the images yields accurate results for tissue level diagnosis of brain cancer called malignant glioma. Second, this work defines a set of global metrics by processing the entire cell-graph to capture tissue level information coded into the histopathological images. In this work, the results are obtained on the photomicrographs of 646 archival brain biopsy samples of 60 different patients. It is shown that the global metrics of cell-graphs distinguish cancerous tissues from noncancerous ones with high accuracy (at least 99 percent accuracy for healthy tissues with lower cellular density level, and at least 92 percent accuracy for benign tissues with similar high cellular density level such as nonneoplastic reactive/inflammatory conditions).