title: Applications of neural networks in histopathology.
abstract: ANN methods are one of several nonparametric approaches to classification. They are based on an adaptive, connectionist model of information storage and processing and learn by example rather than explicitly programmed classification rules. Their greatest strength probably is the ease with which they capture nonlinear and interactive feature effects during training, though at a higher risk of overtraining then is found with traditional classifiers. ANN can be used to process image data, but so far not to classify complex natural images directly. Instead, image dimensionality is reduced to a set of extracted features for ANN input. ANN finds growing use in time series signal processing and radiology. In anatomic pathology, ANN pilot studies are published for tumor classification. Pap smear analysis, and chromosome identification. Each application requires proper sampling technique, minimising bias in training and testing to assure the ANN classifier will perform prospectively as expected. With careful arrangement of input features, ANN can be used for prognostic as well as diagnostic models.
authors.affiliation: Dept. of Cellular Pathology, Armed Forces Institute of Pathology, Washington, DC, USA.
authors.forename: R L
authors.initials: RL
authors.lastname: Becker
mesh_terms: D006801:Humans; D019044:Image Cytometry; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D010338:Pathology, Clinical / Q000379:methods*