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Literature DB >> 1292445

Bayesian belief networks in quantitative histopathology.

P H Bartels¹, D Thompson, M Bibbo, J E Weber.

Abstract

Bayesian belief networks have a dynamic range and numeric response characteristics that make them uniquely suitable for descriptive classification schemes. Features showing considerable overlap of tolerance regions may be used, in a cumulative manner, to derive unequivocal classification decisions. The numeric response characteristics of Bayesian belief networks are analyzed, and their application as control modules in automated scene segmentation in histopathology is demonstrated.

Mesh：

Year: 1992 PMID： 1292445

Source DB: PubMed Journal: Anal Quant Cytol Histol ISSN： 0884-6812 Impact factor: 0.302

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Cited

8 in total

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Journal: IEEE Rev Biomed Eng Date: 2009-10-30

2. Expert system support using Bayesian belief networks in the diagnosis of fine needle aspiration biopsy specimens of the breast.

Authors: P W Hamilton; N Anderson; P H Bartels; D Thompson
Journal: J Clin Pathol Date: 1994-04 Impact factor: 3.411

3. Diagnostic distance of high grade prostatic intraepithelial neoplasia from normal prostate and adenocarcinoma.

Authors: R Montironi; R Pomante; P Colanzi; D Thompson; P W Hamilton; P H Bartels
Journal: J Clin Pathol Date: 1997-09 Impact factor: 3.411

4. The development of a decision support system for the pathological diagnosis of human cerebral tumours based on a neural network classifier.

Authors: G Sieben; M Praet; H Roels; G Otte; L Boullart; L Calliauw
Journal: Acta Neurochir (Wien) Date: 1994 Impact factor: 2.216

Bayesian belief networks in quantitative histopathology.

Review 1. Histopathological image analysis: a review.

2. Expert system support using Bayesian belief networks in the diagnosis of fine needle aspiration biopsy specimens of the breast.

3. Diagnostic distance of high grade prostatic intraepithelial neoplasia from normal prostate and adenocarcinoma.

4. The development of a decision support system for the pathological diagnosis of human cerebral tumours based on a neural network classifier.

5. Histopathological image analysis for centroblasts classification through dimensionality reduction approaches.

6. Deep Learning-Based Diagnosis Method of Emergency Colorectal Pathology.

7. Time-frequency time-space long short-term memory networks for image classification of histopathological tissue.

8. Automated discrimination of lower and higher grade gliomas based on histopathological image analysis.