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Epstein–Barr virus-associated gastric cancer shows a robust response to immune checkpoint inhibitors. Here the authors introduce a deep convolutional neural network and its fusion with pathologists for predicting it from histopathology.

Crowd counting plays an important role in urban management and public security. Recently, deep learning has shown a great advantage in making the quality of crowd counting more accurate. However, how to apply deep learning models to embedded terminals is still a challenging issue. The main contradiction of the problem lies in high demand for computing resources for deep learning and strict limitation of power consumption from embedded devices. In order to achieve the crowd counting by edge computing (in embedded terminals), we propose a tiny model based on convolutional neural networks. The model can be switched into other two forms to adapt tradeoff between the accuracy and efficiency. Especially, different forms share main parameters so as to save storage and avoid retraining. What is more, this model supports variant sizes of input images, which benefits the applications to a variety of embedded devices. The experiments on two different embedded terminals have shown the effectiveness, efficiency, and flexibility of our proposed model.

Person re-identification (Re-ID), which is for matching pedestrians across disjoint camera views in surveillance, has made great progress in supervised learning. However, requirement of a large number of labelled identities leads to high cost for large-scale Re-ID systems. Consequently, it is significant to study learning Re-ID with unlabelled data and limited labelled data, that is, semi-supervised person re-identification. When labelled data is limited, the learned model tends to overfit the data and cannot generalize well. Moreover, the scene variations between cameras lead to domain shift in the feature space, which makes mining auxiliary supervision information from unlabelled data more difficult. To address these problems, we propose a Distilled Camera-Aware Self Training framework for semi-supervised person re-identification. To alleviate the overfitting problem for learning from limited labelled data, we propose a Multi-Teacher Selective Similarity Distillation Loss to selectively aggregate the knowledge of multiple weak teacher models trained with different subsets and distill a stronger student model. Then, we exploit the unlabelled data by learning pseudo labels by clustering based on the student model for self training. To alleviate the effect of scene variations between cameras, we propose a Camera-Aware Hierarchical Clustering (CAHC) algorithm to perform intra-camera clustering and cross-camera clustering hierarchically. Experiments show that our method outperformed the state-of-the-art semi-supervised person re-identification methods.