title: Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features.
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 [Formula: see text] 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 [Formula: see text]-measure of 80.31%, while M_ex yielded corresponding accuracy and F-measures of 73.47% and 77.97%, respectively.
authors.affiliation: 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.
authors.forename: Germán
authors.initials: G
authors.lastname: Corredor
authors.affiliation: Case Western Reserve University , Center of Computational Imaging and Personalized Diagnostics, Department of Biomedical Engineering, Cleveland, Ohio, United States.
authors.forename: Jon
authors.initials: J
authors.lastname: Whitney
authors.affiliation: Universidad Nacional de Colombia , Patología Molecular Research Group, Department of Pathology, Bogota, Colombia.
authors.forename: Viviana
authors.initials: V
authors.lastname: Arias
authors.affiliation: Case Western Reserve University , Center of Computational Imaging and Personalized Diagnostics, Department of Biomedical Engineering, Cleveland, Ohio, United States.
authors.forename: Anant
authors.initials: A
authors.lastname: Madabhushi
authors.affiliation: Universidad Nacional de Colombia , Computer Imaging and Medical Applications Lab, Department of Medical Imaging, Bogota, Colombia.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Romero
keywords: basal-cell carcinoma; cancer detection; classification; digital pathology; graphs; implicit relevance feedback; visual attention map