11 March 2017 Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features
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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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Germán Corredor, Germán Corredor, Jon Whitney, Jon Whitney, Viviana L. Arias Pedroza, Viviana L. Arias Pedroza, Anant Madabhushi, Anant Madabhushi, Eduardo Romero Castro, Eduardo Romero Castro, } "Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features," Journal of Medical Imaging 4(2), 021105 (11 March 2017). https://doi.org/10.1117/1.JMI.4.2.021105 . Submission: Received: 23 August 2016; Accepted: 22 February 2017
Received: 23 August 2016; Accepted: 22 February 2017; Published: 11 March 2017
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