Special Section on Digital Pathology

Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features

[+] Author Affiliations
Germán Corredor

Universidad Nacional de Colombia, Computer Imaging and Medical Applications Lab, Department of Medical Imaging, Bogota, Colombia

Case Western Reserve University, Center of Computational Imaging and Personalized Diagnostics, Department of Biomedical Engineering, Cleveland, Ohio, United States

Jon Whitney, Anant Madabhushi

Case Western Reserve University, Center of Computational Imaging and Personalized Diagnostics, Department of Biomedical Engineering, Cleveland, Ohio, United States

Viviana Arias

Universidad Nacional de Colombia, Patología Molecular Research Group, Department of Pathology, Bogota, Colombia

Eduardo Romero

Universidad Nacional de Colombia, Computer Imaging and Medical Applications Lab, Department of Medical Imaging, Bogota, Colombia

J. Med. Imag. 4(2), 021105 (Mar 11, 2017). doi:10.1117/1.JMI.4.2.021105
History: Received August 23, 2016; Accepted February 22, 2017
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Abstract.  Computational histomorphometric approaches typically use low-level image features for building machine learning classifiers. However, these approaches usually ignore high-level expert knowledge. A computational model (M_im) combines low-, mid-, and high-level image information to predict the likelihood of cancer in whole slide images. Handcrafted low- and mid-level features are computed from area, color, and spatial nuclei distributions. High-level information is implicitly captured from the recorded navigations of pathologists while exploring whole slide images during diagnostic tasks. This model was validated by predicting the presence of cancer in a set of unseen fields of view. The available database was composed of 24 cases of basal-cell carcinoma, from which 17 served to estimate the model parameters and the remaining 7 comprised the evaluation set. A total of 274 fields of view of size 1024×1024  pixels were extracted from the evaluation set. Then 176 patches from this set were used to train a support vector machine classifier to predict the presence of cancer on a patch-by-patch basis while the remaining 98 image patches were used for independent testing, ensuring that the training and test sets do not comprise patches from the same patient. A baseline model (M_ex) estimated the cancer likelihood for each of the image patches. M_ex uses the same visual features as M_im, but its weights are estimated from nuclei manually labeled as cancerous or noncancerous by a pathologist. M_im achieved an accuracy of 74.49% and an F-measure of 80.31%, while M_ex yielded corresponding accuracy and F-measures of 73.47% and 77.97%, respectively.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Germán Corredor ; Jon Whitney ; Viviana Arias ; Anant Madabhushi and Eduardo Romero
"Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features", J. Med. Imag. 4(2), 021105 (Mar 11, 2017). ; http://dx.doi.org/10.1117/1.JMI.4.2.021105


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