CLASSIFYING HISTOPATHOLOGY WHOLE-SLIDES USING FUSION OF DECISIONS FROM DEEP CONVOLUTIONAL NETWORK ON A COLLECTION OF RANDOM MULTI-VIEWS AT MULTI-MAGNIFICATION

Histopathology forms the gold standard for confirmed diagnosis of a suspicious hyperplasia being benign or malignant and for its sub-typing. While techniques like whole-slide imaging have enabled computer assisted analysis for exhaustive reporting of the tissue section, it has also given rise to the big-data deluge and the time complexity associated with processing GBs of image data acquired over multiple magnifications. Since preliminary screening of a slide into benign or malignant carried out on the fly during the digitization process can reduce a Pathologist's work load, to devote more time for detailed analysis, slide screening has to be performed on the fly with high sensitivity. We propose a deep convolutional neural network (CNN) based solution, where we analyse images from random number of regions of the tissue section at multiple magnifications without any necessity of view correspondence across magnifications. Further a majority voting based approach is used for slide level diagnosis, i.e., the class posteriori estimate of each views at a particular magnification is obtained from the magnification specific CNN, and subsequently posteriori estimate across random multi-views at multi-magnification are voting filtered to provide a slide level diagnosis. We have experimentally evaluated performance using a patient level 5-folded cross-validation with 5 8 malignant and 2 4 benign cases of breast tumors to obtain average accuracy of 94.67 +/- 14.60 %, sensitivity of 96.00 +/- 8.94 %, specificity of 92.00 +/- 17.85 % and F-score of 96.24 +/- 5.29 % while processing each view in approximate to 10 ms.