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

Germán Corredor; Jon Whitney; Viviana L. Arias Pedroza M.D.; Anant Madabhushi; Eduardo Romero Castro M.D.

doi:10.1117/1.JMI.4.2.021105

11 March 2017 Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features

Germán Corredor, Jon Whitney, Viviana L. Arias Pedroza M.D., Anant Madabhushi, Eduardo Romero Castro M.D.

Author Affiliations +

Journal of Medical Imaging, Vol. 4, Issue 2, 021105 (March 2017). https://doi.org/10.1117/1.JMI.4.2.021105

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.

Citation Download Citation

Germán Corredor, Jon Whitney, Viviana L. Arias Pedroza M.D., Anant Madabhushi, and Eduardo Romero Castro M.D. "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

Received: 23 August 2016; Accepted: 22 February 2017; Published: 11 March 2017

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available