… imaging data of biopsy samples are large in size and complex in nature. Therefore, pathologists
face a substantial workload increase for histopathological … corroborating the idea that …

… The idea is to initialize the neural network with the weights … with similar stains in the histology
images 14 . The negative … , 100 sample size case the average classification error rises …

… recognition based systems to improve the quality of diagnosis. … automatic classification of
breast cancer histology images into … The main idea is to divide the image into levels. Each level …

… Automated segmentation or classification of EP and ST tissues is … Deep Convolutional
Neural Networks (DCNN) based feature learning is presented to automatically segment or classify …

… adaptive sampling because CNN is only able to classify small … tile samples to be classified
by the CNN in order to improve … of the art in image analysis and classification tasks related to …

… classification of breast cancer histopathology images. In this work, we propose a set of training
techniques to improve … The underlying idea behind machine learning is to train computer-…

… This work presents a methodology for the classification of … an increase in the selection of
deep convolutional neural … ATT, NG and CTA conceived of the idea and methodology, AA …

… ] to automatically classify histopathological images can not only improve the diagnostic efficiency,
… , which is based on the idea of sparse support vector machine combined with Wilcoxon …

… This increase indicates that both a high-performance deep … to improve the ability of
histopathological image classification. … The core ideas of these methods are much the same. The …

… examine a huge number of histopathological images to detect … for 40X images, and the rate
increases as the magnification … , we plan to extend this idea to multiclass classification tasks. …