Computer-assisted image classification: use of neural networks in anatomic pathology.
Artificial neural networks (ANN) implemented on digital computers have received much attention for interpretation of images in pathology and cytology. Most such images are too complex for current ANN to interpret directly; instead, ANN usually classify the images according to numeric features extracted from them. In experiments on three distinct image classification problems, ANN classifiers performed as well or better than multivariate linear discriminant analysis (a traditional parametric statistical classifier). ANN empirically define non-linear multivariate decision boundaries, and can combine non-contiguous feature areas in mapping a classification. However, many training cases are required in order to map complex area boundaries precisely and with a low risk of 'overtraining.' Careful problem selection and attention to data dimensionality and format are important for efficient ANN use.
Becker
R L
RL
Department of Cellular Pathology, Armed Forces Institute of Pathology, Washington, DC 20306-6000.
D000230:Adenocarcinoma / Q000175:diagnosis*; D001943:Breast Neoplasms / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D016002:Discriminant Analysis; D005348:Fibrocystic Breast Disease / Q000175:diagnosis*; D006801:Humans; D007962:Leukocytes / Q000145:classification*; D008677:Metaphase; D016571:Neural Networks, Computer