Effects of annotation granularity in deep learning models for histopathological images Pathological is crucial to cancer diagnosis. Usually, Pathologists draw their conclusion based on observed cell and tissue structure on histology slides. Rapid development in machine learning, especially deep learning have established robust and accurate classifiers. They are being used to analyze histopathological slides and assist pathologists in diagnosis. Most machine learning systems rely heavily on annotated data sets to gain experiences and knowledge to correctly and accurately perform various tasks such as classification and segmentation. Generally, annotations made in pathologyrelated datasets have inherited annotation methods from natural scene images. This work investigates different granularity of annotations in histopathological data set including imagewise, bounding box, ellipsewise, and pixelwise to verify the influence of annotation in pathological slide on deep learning models. We design corresponding experiments to test classification and segmentation performance of deep learning models based on annotations with different annotation granularity. In classification, stateoftheart deep learningbased classifiers perform better when trained by pixelwise annotation dataset. On average, precision, recall and F1score improves by 7.87%, 8.83% and 7.85% respectively. Thus, it is suggested that finer granularity annotations are better utilized by deep learning algorithms in classification tasks. Similarly, semantic segmentation algorithms can achieve 8.33% better segmentation accuracy when trained by pixelwise annotations. Our study shows not only that finergrained annotation can improve the performance of deep learning models, but also help they extract more accurate phenotypic information from histopathological slides. The accurate and spatially precise acquisitions of phenotypic information can improve the reliability of the model prediction. Intelligence systems trained on granular annotations may help pathologists inspecting certain regions and features in the slide that were mainly used to calculate the prediction. The compartmentalized prediction approach similar to this work may contribute to phenotype and genotype association studies.

Texturebased Deep Learning for Effective Histopathological Cancer Image Classification Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques, since manual examination and diagnosis with WSIs are time and costconsuming. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. However, despite the success of the development, there are still opportunities for further enhancements. In this paper, we propose a novel cancer texturebased deep neural network (CATNet) that learns scalable morphological features from histopathological WSIs. The innovation of CATNet is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) improving predictive performance while reducing model complexity. Moreover, CATNet can provide discriminative morphological (texture) patterns formed on cancerous regions of histopathological images comparing to normal regions. We elucidated how our proposed method, CATNet, captures morphological patterns of interest in hierarchical levels in the model. The proposed method outperformed the current stateoftheart benchmark methods on accuracy, precision, recall, and F1 score.