Search

Background: Computer-aided medical decision-making (CAMDM) is the method to utilize massive EMR data as both empirical and evidence support for the decision procedure of healthcare activities. Well-developed information infrastructure, such as hospital information systems and disease surveillance systems, provides abundant data for CAMDM. However, the complexity of EMR data with abstract medical knowledge makes the conventional model incompetent for the analysis. Thus a deep belief networks (DBN) based model is proposed to simulate the information analysis and decision-making procedure in medical practice. The purpose of this paper is to evaluate a deep learning architecture as an effective solution for CAMDM., Methods: A two-step model is applied in our study. At the first step, an optimized seven-layer deep belief network (DBN) is applied as an unsupervised learning algorithm to perform model training to acquire feature representation. Then a support vector machine model is adopted to DBN at the second step of the supervised learning. There are two data sets used in the experiments. One is a plain text data set indexed by medical experts. The other is a structured dataset on primary hypertension. The data are randomly divided to generate the training set for the unsupervised learning and the testing set for the supervised learning. The model performance is evaluated by the statistics of mean and variance, the average precision and coverage on the data sets. Two conventional shallow models (support vector machine / SVM and decision tree / DT) are applied as the comparisons to show the superiority of our proposed approach., Results: The deep learning (DBN + SVM) model outperforms simple SVM and DT on two data sets in terms of all the evaluation measures, which confirms our motivation that the deep model is good at capturing the key features with less dependence when the index is built up by manpower., Conclusions: Our study shows the two-step deep learning model achieves high performance for medical information retrieval over the conventional shallow models. It is able to capture the features of both plain text and the highly-structured database of EMR data. The performance of the deep model is superior to the conventional shallow learning models such as SVM and DT. It is an appropriate knowledge-learning model for information retrieval of EMR system. Therefore, deep learning provides a good solution to improve the performance of CAMDM systems., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Learning generalized feature embedding is crucial for various computer vision tasks, including domain generalizable person re-identification (ReID). ReID aims to develop deep learning-based feature embeddings that can effectively recognize individuals in both trained (source) domains and unseen target domains. However, many state-of-the-art ReID methods suffer from overfitting as they train and test within the same source domain. To address this issue, we investigate the potential of multi-granularity approaches in mitigating domain shift challenges in person re-identification. Specifically, we propose a novel framework called instance-guided multi-granularity (IGMG), which leverages style-free features through non-parametric Instance Normalization (IN) at multiple granularity levels. While high-level abstract concepts are often not shared across different classes, low- and mid-level features can offer more shareable information to enhance the model's generalization capabilities. By incorporating this concept, our framework can dynamically eliminate style variations across various levels of abstraction. As a result, it enables the model to capture fine-grained details and high-level semantics, leading to enhanced robustness against changes in data distribution. To validate the effectiveness of our approach, we conduct extensive experiments on multiple benchmark ReID datasets. The results consistently demonstrate that our proposed framework exhibits strong generalization capabilities, performing consistently well on unseen target domains. The code is available at https://github.com/mdamranhossenbhuiyan/IGMG/. [Display omitted] • Unique CNN for re-identification, merges high-level semantics with fine details. • First to use non-parametric instance normalization for style-free features in ReID. • Leveraged non-parametric IN module for style-content split, fixing style variance. [ABSTRACT FROM AUTHOR]

Video-based re-identification (ReID) is a crucial task in computer vision that draws increasing attention due to advances in deep learning (DL) and modern computational devices. Despite recent success with CNN architectures, single models (e.g., 2D-CNNs or 3D-CNNs) alone failed to leverage temporal information with spatial cues. This is due to uncontrolled surveillance scenarios and variable poses leading to inevitable misalignment of ROIs across the tracklets, which is accompanied by occlusion and motion blur. In this context, designing temporal and spatial cues for two different models and their combinations can be beneficial, considering the global of a video-tracklet. 3D-CNNs allow encoding of temporal information while 2D-CNNs extract spatial or appearance information. In this paper, we propose a Spatio-Temporal Cross Attention (STCA) network to utilize both 2D-CNNs and 3D-CNNs that calculate the cross attention mapping both from the layer of 3D-CNNs and 2D-CNNs along a person's trajectory to gate the following layers of 2D-CNNs; and highlight relevant appearance features for the person ReID. Given an input tracklet, the proposed cross attention (CA) is able to capture the salient regions that propagate throughout the tracklet to obtain the global view. This provides a spatio-temporal attention approach that can be dynamically aggregated with spatial features of 2D-CNNs to perform finer-grained recognition. Additionally, we exploit the advantage of utilizing cosine similarity while triplet sampling as well as for calculating the final recognition score. Experimental analyses on three challenging benchmark datasets indicate that integrating spatio-temporal cross attention into the state-of-the-art video ReID backbone CNN architecture allows for improving their recognition accuracy. [Display omitted] • We propose a Spatio Temporal Cross Attention (STCA) network to generate cross guided attention for video re-identification. • The proposed STCA adopts both the 2D and 3D-CNNs to capture the common salient features consistent throughout space and time. • The generated attention is used for gating 2D-CNN that enhances its mean of fine-grained recognition to address misalignment. • Optimizing STCA using cosine distance for hard triplet mining leads to faster convergence and better recognition accuracy [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis has obtained great success in recent years. Most of the existing work focuses on determining the sentiment polarity of the given aspect according to the given text, while little attention has been paid to the visual information as well as multimodality content for aspect-based sentiment analysis. Multimodal content is becoming increasingly popular in mainstream online social platforms and can help better extract user sentiments toward a given aspect. There are only few studies focusing on this new task: Multimodal Aspect-based Sentiment Analysis (MASA), which performs aspect-based sentiment analysis by integrating both texts and images. In this paper, we propose a mutimodal interaction model for MASA to learn the relationship among the text, image and aspect via interaction layers and adversarial training. Additionally, we build a new large-scale dataset for this task, named MASAD , which involves seven domains and 57 aspect categories with 38 k image-text pairs. Extensive experiments have been conducted on the proposed dataset to provide several baselines for this task. Though our models obtain significant improvement for this task, empirical results show that MASA is more challenging than textual aspect-based sentiment analysis, which indicates that MASA remains a challenging open problem and requires further efforts. [ABSTRACT FROM AUTHOR]