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In this paper, we introduced a novel feature extraction approach, namedexclusive autoencoder (XAE), which is a supervised version of autoencoder (AE),able to largely improve the performance of nucleus detection and classificationon hematoxylin and eosin (H&E) histopathological images. The proposed XAE canbe used in any AE-based algorithm, as long as the data labels are also providedin the feature extraction phase. In the experiments, we evaluated theperformance of an approach which is the combination of an XAE and a fullyconnected neural network (FCN) and compared with some AE-based methods. For anucleus detection problem (considered as a nucleus/non-nucleus classificationproblem) on breast cancer H&E images, the F-score of the proposed XAE+FCNapproach achieved 96.64% while the state-of-the-art was at 84.49%. For nucleusclassification on colorectal cancer H&E images, with the annotations of fourcategories of epithelial, inflammatory, fibroblast and miscellaneous nuclei.The F-score of the proposed method reached 70.4%. We also proposed a lymphocytesegmentation method. In the step of lymphocyte detection, we have compared withcutting-edge technology and gained improved performance from 90% to 98.67%. Wealso proposed an algorithm for lymphocyte segmentation based on nucleusdetection and classification. The obtained Dice coefficient achieved 88.31%while the cutting-edge approach was at 74%.

eXclusive Autoencoder (XAE) for Nucleus Detection and Classification on Hematoxylin and Eosin (H&E) Stained Histopathological Images