Video summarization via global feature difference optimization

Video summarization aims at selecting valuable clips for browsing videos with high efficiency.Previous approaches typically focus on aggregating temporal features while ignoring the potential role of visual representations in summarizing videos.In this paper,we present a global difference-aware network(GDANet)that exploits the feature difference across frame and video as guidance to enhance visual features.Initially,a difference optimization module(DOM)is devised to enhance the discriminability of visual features,bringing gains in accurately aggregating temporal cues.Subsequently,a dual-scale attention module(DSAM)is introduced to capture informative contextual information.Eventually,we design an adaptive feature fusion module(AFFM)to make the network adaptively learn context representations and perform feature fusion effectively.We have conducted experiments on benchmark datasets,and the empirical results demonstrate the effectiveness of the proposed framework.

A Deep Learning Framework for Mass-Forming Chronic Pancreatitis and Pancreatic Ductal Adenocarcinoma Classification Based on Magnetic Resonance Imaging

Pancreatic diseases, including mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma(PDAC), present with similar imaging features, leading to diagnostic complexities. Deep Learning (DL) methodshave been shown to perform well on diagnostic tasks. Existing DL pancreatic lesion diagnosis studies basedon Magnetic Resonance Imaging (MRI) utilize the prior information to guide models to focus on the lesionregion. However, over-reliance on prior information may ignore the background information that is helpful fordiagnosis. This study verifies the diagnostic significance of the background information using a clinical dataset.Consequently, the Prior Difference Guidance Network (PDGNet) is proposed, merging decoupled lesion andbackground information via the Prior Normalization Fusion (PNF) strategy and the Feature Difference Guidance(FDG) module, to direct the model to concentrate on beneficial regions for diagnosis. Extensive experiments inthe clinical dataset demonstrate that the proposed method achieves promising diagnosis performance: PDGNetsbased on conventional networks record an ACC (Accuracy) and AUC (Area Under the Curve) of 87.50% and89.98%, marking improvements of 8.19% and 7.64% over the prior-free benchmark. Compared to lesion-focusedbenchmarks, the uplift is 6.14% and 6.02%. PDGNets based on advanced networks reach an ACC and AUC of89.77% and 92.80%. The study underscores the potential of harnessing background information in medical imagediagnosis, suggesting a more holistic view for future research.

An analysis of clinical features of celiac disease patients in different ethnic

Objective To summarize the clinical of different racial patients with celiac disease(CD)and analyze the disease prevalence,diagnosis and treatment in Chinese population.Methods All the patients were diagnosed as CD and enrolled in Beijing United Family Hospital between January 2005 and July 2015.Clinical data including nationality,age,symptoms,endoscopic and patho-