Proceedings Article | 6 July 2018
KEYWORDS: Breast, Image classification, Diagnostics, Databases, Pathology, Cancer, Neural networks, Breast cancer, Machine learning, Image segmentation
Studies have shown that there are discrepancies among pathologists in the classification of breast histopathological slides. In this study we propose a framework for categorizing hematoxylin-eosin stained breast images either as benign or malignant at four magnification factors, and then aggregating the classification results of a patient’s images from different magnification factors to make the ultimate diagnosis for each patient. We used a publicly available database, containing 7786 images from 81 patients. The images were acquired in four visual magnification factors, namely x40, x100, x200, and x400, with an effective pixel size of 0.49 μm, 0.20 μm, 0.10 μm, and 0.05 μm respectively. In order to mitigate the inconsistencies in the color of the images, stain normalization was performed. Next, for each magnification factor, a deep residual network (ResNet) with 152 layers has been trained for classifying patches from the images as benign or malignant. Then, a meta-decision tree was used to combine classification results of a patient’s images from different magnification factors to provide a patient-level diagnosis. The ResNets achieved correct classification rates (CCR) of 98.52%, 97.90%, 98.33%, and 97.66% at x40, x100, x200, and x400 magnification factors, respectively. For classification of patients either as benign or malignant, a CCR of 98.77% was obtained. In conclusion, our study showed that the proposed framework can be helpful in the categorization of breast digital slides.
