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18 March 2019 Computer aided antibody screening for IHC assay development
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Antibody development is crucial for immunohistochemistry (IHC) applications. To improve the efficiency of primary antibody screening processes, we developed a computer aided detection scheme to automatically identify the non-negative tissue slides which indicate reactive antibodies. A dataset with 564 digital IHC whole slide images were used for algorithm training and testing, each of which was labeled by pathologist as a negative (i.e., no staining) or non-negative (i.e., pure background or partial staining) slide. To avoid unnecessary computations, color deconvolution was first applied to low resolution whole slide images and histogram based image features were extracted from each unmixed single stain image. Then, different classifiers were built using the low resolution image features computed from the training dataset through ten-fold cross validation. The trained model was tested over the testing dataset. Results indicated that linear supported vector machine (LSVM) method yielded the highest area under ROC curve. To further improve the accuracy, our scheme utilized the LSVM classifier score to identify the slides for which additional analysis was needed. The additional analysis was performed through dividing the original whole slide image into non-overlapping tiles and extracting high resolution image features from each tile. The tile-based features are then used to form a bag-of-words (BoW) representation of the corresponding whole slide image, based on which a second classifier was built to perform the predictions. The results showed that the proposed scheme can effectively perform negative versus non-negative classification with high accuracy and thus reduce pathologists’ manual reviewing time for antibody screening.
Conference Presentation
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Faranak Aghaei and Yao Nie "Computer aided antibody screening for IHC assay development", Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 109560C (18 March 2019);

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