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  • The individual PPVs for the strong, moderate, and weak categories were reported as 90.5%, 88%, and 88%, respectively, which is in line with what is observed in this experiment. The results from this experiment on 14 test regions, as well as the previous experiments on 1200 individual cells (32 × 32 blocks) proved that the proposed WMS classification algorithm is reliable in classifying the positive cells into the three staining strengths. Figure 8 shows several visual examples of the final detection and classification results, while Figure 9 shows the proposed algorithms integrated into our ER-IHC breast carcinoma assessment system, for use in prognostic applications. It is possible that the pathologist misses some positive cells due to the sheer size of the images, so the rule may be very useful. The only other image with a score difference of more than 2 is image 05441, where the pathologists estimated an ER status of less than 1%, while the computer predicted it to be close to 50%. Upon detailed inspection, it was found that this particular image was a rather brownish in general compared to other images with similar status, which could be caused by an error during the staining process. Overall, the proposed approach managed to predict the scores similar to the pathologists, except for a few cases explained earlier. We have proposed a cell detection and classification system based on a convolutional neural network model for use with the Allred scoring system for breast carcinoma hormone receptor status testing. The system classifies each cell in the ER-stained whole-slide images into negatively, weakly, moderately, and strongly stained cells, before Allred scoring is carried out to recommend hormonal treatment options. To the best of our knowledge, this is the first work of its kind in developing such a system, which would be a valuable tool for histopathologists in improving the reliability of predictive tumor marker reporting as well as reducing manual intervention workload. The cell detection and classification were applied on 40 whole-slide images before Allred scoring was carried out to recommend hormonal treatment options. Experimental result shows very promising observations for both the detection and classification processes, as well as the Allred scoring computation. The automated Allred scoring matches well with pathologists scoring, for both the actual Allred score and hormonal treatment cases.

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