BackgroundHistopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited.ResultsIn this paper, we propose leveraging deep convolutional neural network (CNN) activation features to perform classification, segmentation and visualization in large-scale tissue histopathology images. Our framework transfers features extracted from CNNs trained by a large natural image database, ImageNet, to histopathology images. We also explore the characteristics of CNN features by visualizing the response of individual neuron components in the last hidden layer. Some of these characteristics reveal biological insights that have been verified by pathologists. According to our experiments, the framework proposed has shown state-of-the-art performance on a brain tumor dataset from the MICCAI 2014 Brain Tumor Digital Pathology Challenge and a colon cancer histopathology image dataset.ConclusionsThe framework proposed is a simple, efficient and effective system for histopathology image automatic analysis. We successfully transfer ImageNet knowledge as deep convolutional activation features to the classification and segmentation of histopathology images with little training data. CNN features are significantly more powerful than expert-designed features.

We propose a simple, efficient and effective method using deep convolutional activation features (CNNs) to achieve stat-of-the-art classification and segmentation for the MICCAI 2014 Brain Tumor Digital Pathology Challenge. Common traits of such medical image challenges are characterized by large image dimensions (up to the gigabyte size of an image), a limited amount of training data, and significant clinical feature representations. To tackle these challenges, we transfer the features extracted from CNNs trained with a very large general image database to the medical image challenge. In this paper, we used CNN activations trained by ImageNet to extract features (4096 neurons, 13.3% active). In addition, feature selection, feature pooling, and data augmentation are used in our work. Our system obtained 97.5% accuracy on classification and 84% accuracy on segmentation, demonstrating a significant performance gain over other participating teams.

No abstract

Polysaccharide from marine shellfish has various bioactivities. In this study, the effects of polysaccharide from Patinopecten yessoensis skirt (PS) on boosting immune response in mice were evaluated, and the potential mechanisms were explored. The results showed that PS administration effectively increased the serum IgG and IgM levels, implying that PS had immune response-boosting properties. Moreover, PS administration could modulate the composition of the gut microbiota, and significantly improve short-chain fatty acids (SCFAs) metabolism, especially butyrate metabolism. Of note, the expression of the Tlr2, Tlr7, MyD88, Tnfa, and Il1b genes in toll-like receptor (TLR) signaling pathway was significantly increased. In summary, PS could boost immune response by modulating the gut microbiota and SCFAs metabolism correlating with the activation of the TLR signaling pathway. Therefore, PS can be developed as a special ingredient for functional product.