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Quantitative analysis of clinical function parameters from MRI images is crucial for diagnosing and assessing cardiovascular disease. However, the manual calculation of these parameters is challenging due to the high variability among patients and the time‐consuming nature of the process. In this study, the authors introduce a framework named MultiJSQ, comprising the feature presentation network (FRN) and the indicator prediction network (IEN), which is designed for simultaneous joint segmentation and quantification. The FRN is tailored for representing global image features, facilitating the direct acquisition of left ventricle (LV) contour images through pixel classification. Additionally, the IEN incorporates specifically designed modules to extract relevant clinical indices. The authors’ method considers the interdependence of different tasks, demonstrating the validity of these relationships and yielding favourable results. Through extensive experiments on cardiac MR images from 145 patients, MultiJSQ achieves impressive outcomes, with low mean absolute errors of 124 mm2, 1.72 mm, and 1.21 mm for areas, dimensions, and regional wall thicknesses, respectively, along with a Dice metric score of 0.908. The experimental findings underscore the excellent performance of our framework in LV segmentation and quantification, highlighting its promising clinical application prospects.

Identification of characteristic points in physiological signals, such as the peak of the R wave in the electrocardiogram and the peak of the systolic wave of the photopletismogram, is a fundamental step for the quantification of clinical parameters, such as the pulse transit time. In this work, we presented a novel neural architecture, called eMTUnet, to automate point identification in multivariate signals acquired with a chest-worn device. The eMTUnet consists of a single deep network capable of performing three tasks simultaneously: (i) localization in time of characteristic points (labeling task), (ii) evaluation of the quality of signals (classification task); (iii) estimation of the reliability of classification (reliability task). Preliminary results in overnight monitoring showcased the ability to detect characteristic points in the four signals with a recall index of about 1.00, 0.90, 0.90, and 0.80, respectively. The accuracy of the signal quality classification was about 0.90, on average over four different classes. The average confidence of the correctly classified signals, against the misclassifications, was 0.93 vs. 0.52, proving the worthiness of the confidence index, which may better qualify the point identification. From the achieved outcomes, we point out that high-quality segmentation and classification are both ensured, which brings the use of a multi-modal framework, composed of wearable sensors and artificial intelligence, incrementally closer to clinical translation.

In applications such as satellite remote sensing and aerial photography, imaging equipment must capture brightness information of different ground scenes within a restricted dynamic range. Due to camera sensor limitations, captured images can represent only a portion of such information, which results in lower resolution and lower dynamic range compared with real scenes. Image super resolution (SR) and multiple-exposure image fusion (MEF) are commonly employed technologies to address these issues. Nonetheless, these two problems are often researched in separate directions. In this paper, we propose MEFSR-GAN: an end-to-end framework based on generative adversarial networks that simultaneously combines super-resolution and multiple-exposure fusion. MEFSR-GAN includes a generator and two discriminators. The generator network consists of two parallel sub-networks for under-exposure and over-exposure, each containing a feature extraction block (FEB), a super-resolution block (SRB), and several multiple-exposure feedback blocks (MEFBs). It processes low-resolution under- and over-exposed images to produce high-resolution high dynamic range (HDR) images. These images are evaluated by two discriminator networks, driving the generator to generate realistic high-resolution HDR outputs through multi-goal training. Extensive qualitative and quantitative experiments were conducted on the SICE dataset, yielding a PSNR of 24.821 and an SSIM of 0.896 for 2× upscaling. These results demonstrate that MEFSR-GAN outperforms existing methods in terms of both visual effects and objective evaluation metrics, thereby establishing itself as a state-of-the-art technology.

Pedestrians are often occluded by various obstacles in public places, which is a big challenge for person re-identification. To alleviate the occlusion problem, we propose a Pose-drive Attention Fusion Mechanism (PAFM) that jointly fuses the discriminative features with pose-driven attention and spatial attention in an end-to-end framework. To simultaneously use global and local features, a multi-task network is constructed to realize multi-granularity feature representation. After anchoring the region of interest to the un-occluded spatial semantic information in the image through the spatial attention mechanism, some key points of the pedestrian’s body are extracted using pose estimation and then fused with the spatial attention map to eliminate the harm of occlusion to the re-identification. Besides, the identification granularity is increased by matching the local features. We test and verify the effectiveness of the PAFM on Occluded-DukeMTMC, Occluded-REID and Partial-REID. The experimental results show that the proposed method has achieved competitive performance to the state-of-the-art methods.

Artificial Intelligence (AI) based ultrasound image processing is quite important for diagnosis of renal tumors. Traditional networks, however, struggle with accurate lesion segmentation and differentiation between benign and malignant cases in complex ultrasound images. This paper introduces an advanced multitask network model to overcome these limitations. Our model combines a deep separable Convolutional ResNet with a NestedUNet, enriched with a Convolutional Block Attention Module (CBAM) and residual connections, enhancing its precision in lesion segmentation and classification in intricate images. Compared with the existing methods, the model proposed in this study has the following innovative contributions: (1) combining depthwise separable convolution and NestedUNet, the computational efficiency and feature extraction ability of the model are significantly improved; (2) The Convolutional Block Attention Module (CBAM) is introduced to enhance the attention to key features, to effectively distinguish lesions in complex backgrounds; (3) The gradient flow is improved by the residual connection, which ensures the stable training of the deep network. Experimental results show that the IoU of the model on the kidney tumor dataset reaches 94.23%, which is significantly better than that of STUNet (92.31%). These results show that the model proposed in this study has significant advantages in processing complex ultrasound images, providing a more accurate basis for clinical diagnosis, and has a high application potential.

Esophagogastroduodenoscopy (EGD) has been widely applied for gastrointestinal (GI) examinations. However, there is a lack of mature technology to evaluate the quality of the EGD inspection process. In this Letter, the authors design a multi-task anatomy detection convolutional neural network (MT-AD-CNN) to evaluate the EGD inspection quality by combining the detection task of the upper digestive tract with ten anatomical structures and the classification task of informative video frames. The authors’ model is able to eliminate non-informative frames of the gastroscopic videos and detect the anatomies in real time. Specifically, a sub-branch is added to the detection network to classify NBI images, informative and non-informative images. By doing so, the detected box will be only displayed on the informative frames, which can reduce the false-positive rate. They can determine the video frames on which each anatomical location is effectively examined, so that they can analyse the diagnosis quality. Their method reaches the performance of 93.74% mean average precision for the detection task and 98.77% accuracy for the classification task. Their model can reflect the detailed circumstance of the gastroscopy examination process, which shows application potential in improving the quality of examinations.

Traffic flow prediction in a given area is often influenced by the interactions with complex dependencies among multiple areas. By far, it remains unexplored to obtain interactive information. To address the issue, MSTMN was proposed, a multi-task learning framework that jointly learns interactive information and spatiotemporal dependencies across tasks. MSTMN consists of a node network, an edge network, and a prediction network. The node network and edge network were trained using the proposed meta-fully convolutional blocks to extract interactive features and generalizable features. The prediction network employed the meta-gated fusion and the recalibration block to both integrate these learned features and external factors. This ensures that the features capture optimal interaction information during the training phase. The proposed model was validated on two real-world movement-on-demand traffic datasets collected in Xiamen, China. Experimental results showed that MSTMN improved performance by 38.42% and 31.77% for one-step and multi-step prediction compared to the state-of-the-art baseline.

Autoimmune gastritis (AIG) has a strong correlation with gastric neuroendocrine tumors (NETs) and gastric cancer, making its timely and accurate diagnosis crucial for tumor prevention. The endoscopic manifestations of AIG differ from those of gastritis caused by Helicobacter pylori (H. pylori) infection in terms of the affected gastric anatomical regions and the pathological characteristics observed in biopsy samples. Therefore, when diagnosing AIG based on endoscopic images, it is essential not only to distinguish between normal and atrophic gastric mucosa but also to accurately identify the anatomical region in which the atrophic mucosa is located. In this study, we propose a patient-based multitask gastroscopy image classification network that analyzes all images obtained during the endoscopic procedure. First, we employ the Scale-Invariant Feature Transform (SIFT) algorithm for image registration, generating an image similarity matrix. Next, we use a hierarchical clustering algorithm to group images based on this matrix. Finally, we apply the RepLKNet model, which utilizes large-kernel convolution, to each image group to perform two tasks: anatomical region classification and lesion recognition. Our method achieves an accuracy of 93.4 ± 0.5% (95% CI) and a precision of 92.6 ± 0.4% (95% CI) in the anatomical region classification task, which categorizes images into the fundus, body, and antrum. Additionally, it attains an accuracy of 90.2 ± 1.0% (95% CI) and a precision of 90.5 ± 0.8% (95% CI) in the lesion recognition task, which identifies the presence of gastric mucosal atrophic lesions in gastroscopy images. These results demonstrate that the proposed multitask patient-based gastroscopy image analysis method holds significant practical value for advancing computer-aided diagnosis systems for atrophic gastritis and enhancing the diagnostic accuracy and efficiency of AIG.

As IT technology advances, the number and types of applications, such as SNS, content, and shopping, have increased across various fields, leading to the emergence of complex and diverse application traffic. As a result, the demand for effective network operation, management, and analysis has increased. In particular, service or application traffic classification research is an important area of study in network management. Web services are composed of a combination of multiple applications, and one or more application traffic can be mixed within service traffic. However, most existing research only classifies application traffic by service unit, resulting in high misclassification rates and making detailed management impossible. To address this issue, this paper proposes three multitask learning methods for application traffic classification using the relationships among tasks composed of browsers, protocols, services, and application units. The proposed methods aim to improve classification performance under the assumption that there are relationships between tasks. Experimental results demonstrate that by utilizing relationships between various tasks, the proposed method can classify applications with 4.4%p higher accuracy. Furthermore, the proposed methods can provide network administrators with information about multiple perspectives with high confidence, and the generalized multitask methods are freely portable to other backbone networks.

Optical‐resolution photoacoustic microscopy (OR‐PAM) is an excellent modality for in vivo biomedical imaging as it noninvasively provides high‐resolution morphologic and functional information without the need for exogenous contrast agents. However, the high excitation laser dosage, limited imaging speed, and imperfect image quality still hinder the use of OR‐PAM in clinical applications. The laser dosage, imaging speed, and image quality are mutually restrained by each other, and thus far, no methods have been proposed to resolve this challenge. Here, a deep learning method called the multitask residual dense network is proposed to overcome this challenge. This method utilizes an innovative strategy of integrating multisupervised learning, dual‐channel sample collection, and a reasonable weight distribution. The proposed deep learning method is combined with an application‐targeted modified OR‐PAM system. Superior images under ultralow laser dosage (32‐fold reduced dosage) are obtained for the first time in this study. Using this new technique, a high‐quality, high‐speed OR‐PAM system that meets clinical requirements is now conceivable. A multitask residual dense network (MT‐RDN) is proposed to meet the challenges in opticalresolution photoacoustic microscopy (OR‐PAM) translational research when laser dosage is confined. The MTRDN integrates multisupervised learning, dualchannel sample collection, and a reasonable weight distribution. Superior images under ultralow laser dosage (32‐fold reduced dosage) are obtained. Using this new technique, OR‐PAM that meets clinical requirements is conceivable.