Publications by Year: 2016
The assessment of protein expression in immunohistochemistry (IHC) images provides important diagnostic, prognostic and predictive information for guiding cancer diagnosis and therapy. Manual scoring of IHC images represents a logistical challenge, as the process is labor intensive and time consuming. Since the last decade, computational methods have been developed to enable the application of quantitative methods for the analysis and interpretation of protein expression in IHC images. These methods have not yet replaced manual scoring for the assessment of IHC in the majority of diagnostic laboratories and in many large-scale research studies. An alternative approach is crowdsourcing the quantification of IHC images to an undefined crowd. The aim of this study is to quantify IHC images for labeling of ER status with two different crowdsourcing approaches, image labeling and nuclei labeling, and compare their performance with automated methods. Crowdsourcing-derived scores obtained greater concordance with the pathologist interpretations for both image labeling and nuclei labeling tasks (83% and 87%), as compared to the pathologist concordance achieved by the automated method (81%) on 5,483 TMA images from 1,909 breast cancer patients. This analysis shows that crowdsourcing the scoring of protein expression in IHC images is a promising new approach for large scale cancer molecular pathology studies.
The International Symposium on Biomedical Imaging
(ISBI) held a grand challenge to evaluate computational
systems for the automated detection of metastatic breast
cancer in whole slide images of sentinel lymph node biopsies.
Our team won both competitions in the grand challenge,
obtaining an area under the receiver operating curve
(AUC) of 0.925 for the task of whole slide image classification
and a score of 0.7051 for the tumor localization task.
A pathologist independently reviewed the same images, obtaining
a whole slide image classification AUC of 0.966 and
a tumor localization score of 0.733. Combining our deep
learning system’s predictions with the human pathologist’s
diagnoses increased the pathologist’s AUC to 0.995, representing
an approximately 85 percent reduction in human
error rate. These results demonstrate the power of using
deep learning to produce significant improvements in the
accuracy of pathological diagnoses.