A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging.

Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years.

Identifying Key Opinion Leaders in Social Media via Modality-Consistent Harmonized Discriminant Embedding.

The digital age has empowered brands with new and more effective targeted marketing tools in the form of key opinion leaders (KOLs). Because of the KOLs' unique capability to draw specific types of audience and cultivate long-term relationship with them, correctly identifying the most suitable KOLs within a social network is of great importance, and sometimes could govern the success or failure of a brand's online marketing campaigns. However, given the high dimensionality of social media data, conducting effective KOL identification by means of data mining is especially challenging. Owing to the generally multiple modalities of the user profiles and user-generated content (UGC) over the social networks, we can approach the KOL identification process as a multimodal learning task, with KOLs as a rare yet far more important class over non-KOLs in our consideration. In this regard, learning the compact and informative representation from the high-dimensional multimodal space is crucial in KOL identification. To address this challenging problem, in this paper, we propose a novel subspace learning algorithm dubbed modality-consistent harmonized discriminant embedding (MCHDE) to uncover the low-dimensional discriminative representation from the social media data for identifying KOLs. Specifically, MCHDE aims to find a common subspace for multiple modalities, in which the local geometric structure, the harmonized discriminant information, and the modality consistency of the dataset could be preserved simultaneously. The above objective is then formulated as a generalized eigendecomposition problem and the closed-form solution is obtained. Experiments on both synthetic example and a real-world KOL dataset validate the effectiveness of the proposed method.

Contextual Correlation Preserving Multiview Featured Graph Clustering.

Graph clustering, which aims at discovering sets of related vertices in graph-structured data, plays a crucial role in various applications, such as social community detection and biological module discovery. With the huge increase in the volume of data in recent years, graph clustering is used in an increasing number of real-life scenarios. However, the classical and state-of-the-art methods, which consider only single-view features or a single vector concatenating features from different views and neglect the contextual correlation between pairwise features, are insufficient for the task, as features that characterize vertices in a graph are usually from multiple views and the contextual correlation between pairwise features may influence the cluster preference for vertices. To address this challenging problem, we introduce in this paper, a novel graph clustering model, dubbed contextual correlation preserving multiview featured graph clustering (CCPMVFGC) for discovering clusters in graphs with multiview vertex features. Unlike most of the aforementioned approaches, CCPMVFGC is capable of learning a shared latent space from multiview features as the cluster preference for each vertex and making use of this latent space to model the inter-relationship between pairwise vertices. CCPMVFGC uses an effective method to compute the degree of contextual correlation between pairwise vertex features and utilizes view-wise latent space representing the feature-cluster preference to model the computed correlation. Thus, the cluster preference learned by CCPMVFGC is jointly inferred by multiview features, view-wise correlations of pairwise features, and the graph topology. Accordingly, we propose a unified objective function for CCPMVFGC and develop an iterative strategy to solve the formulated optimization problem. We also provide the theoretical analysis of the proposed model, including convergence proof and computational complexity analysis. In our experiments, we extensively compare the proposed CCPMVFGC with both classical and state-of-the-art graph clustering methods on eight standard graph datasets (six multiview and two single-view datasets). The results show that CCPMVFGC achieves competitive performance on all eight datasets, which validates the effectiveness of the proposed model.