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With the popularization of computer-aided diagnosis (CAD) technologies, more and more deep learning methods are developed to facilitate the detection of ophthalmic diseases. In this article, the deep learning-based detections for some common eye diseases, including cataract, glaucoma, and age-related macular degeneration (AMD), are analyzed. Generally speaking, morphological change in retina reveals the presence of eye disease. Then, while using some existing deep learning methods to achieve this analysis task, the satisfactory performance may not be given, since fundus images usually suffer from the impact from data imbalance and outliers. It is, therefore, expected that with the exploration of effective and robust deep learning algorithms, the detection performance could be further improved. Here, we propose a deep learning model combined with a novel mixture loss function to automatically detect eye diseases, through the analysis of retinal fundus color images. Specifically, given the good generalization and robustness of focal loss and correntropy-induced loss functions in addressing complex dataset with class imbalance and outliers, we present a mixture of those two losses in deep neural network model to improve the recognition performance of classifier for biomedical data. The proposed model is evaluated on a real-life ophthalmic dataset. Meanwhile, the performance of deep learning model with our proposed loss function is compared with the baseline models, while adopting accuracy, sensitivity, specificity, Kappa, and area under the receiver operating characteristic curve (AUC) as the evaluation metrics. The experimental results verify the effectiveness and robustness of the proposed algorithm.Since electroencephalogram (EEG) signals can truly reflect human emotional state, emotion recognition based on EEG has turned into a critical branch in the field of artificial intelligence. Aiming at the disparity of EEG signals in various emotional states, we propose a new deep learning model named three-dimension convolution attention neural network (3DCANN) for EEG emotion recognition in this paper. The 3DCANN model is composed of spatio-temporal feature extraction module and EEG channel attention weight learning module, which can extract the dynamic relation well among multi-channel EEG signals and the internal spatial relation of multi-channel EEG signals during continuous time period. Selleckchem Glesatinib In this model, the spatio-temporal features are fused with the weights of dual attention learning, and the fused features are input into softmax classifier for emotion classification. In addition, we utilize SJTU Emotion EEG Dataset (SEED) to appraise the feasibility and effectiveness of the proposed algorithm. Finally, experimental results display that the 3DCANN method has superior performance over the state-of-the-art models in EEG emotion recognition.Deep learning; transfer learning; ensemble learning; Alzheimer's disease.COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful information for COVID-19 diagnosis. Although several deep learning-based segmentation methods have been proposed for COVID-19 segmentation and have achieved state-of-the-art results, the segmentation accuracy is still not high enough (approximately 85%) due to the variations COVID-19 infected areas (such as shape and size variations) and the similarities between COVID-19 and non-COVID-19 infected areas. To improve the segmentation accuracy of COVID-19 infected areas, we propose an interactive attention refinement network (Attention RefNet). This network is integrated with a backbone segmentation network to refine the initial segmentation resulting from the backbone segmentation network. There are three contributions of this paper, as follows. First, we propose an interactive attention refinement network, which can be connected with any segmentation network and trained with the segmentation network in an end-to-end fashion. Second, we propose a skip connection attention module to improve the important features in both segmentation and refinement networks for initial segmentation and refinement. Ultimately, we propose a seed point module to enhance the important seeds (positions) for interactive refinement. The effectiveness of the proposed method was demonstrated on public datasets (COVID-19CTSeg and MICCAI) and our private multicenter dataset. The segmentation accuracy was improved to more than 90%. We also confirmed the generalizability of the proposed network on our multicenter dataset. The proposed method can still achieve high segmentation accuracy. The model can even be applied to datasets from other centers that are collected in different hospitals and were not included in the training dataset.Many successful semantic segmentation models trained on certain datasets experience a performance gap when they are applied to the actual scene images, expressing weak robustness of these models in the actual scene. The training task conversion (TTC) and domain adaption field have been originally proposed to solve the performance gap problem. Unfortunately, many existing models for TTC and domain adaptation have defects, and even if the TTC is completed, the performance is far from the original task model. Thus, how to maintain excellent performance while completing TTC is the main challenge. In order to address this challenge, a deep learning model named DLnet is proposed for TTC from the existing image dataset-based training task to the actual scene image-based training task. The proposed network, named the DLnet, contains three main innovations. The proposed network is verified by experiments. The experimental results show that the proposed DLnet not only can achieve state-of-the-art quantitative performance on four popular datasets but also can obtain outstanding qualitative performance in four actual urban scenes, which demonstrates the robustness and performance of the proposed DLnet. In addition, although the proposed DLnet cannot achieve outstanding performance in real time, it can still achieve a moderate performance in real time, which is within an acceptable range.We present a CMOS biochip-based photometer for quantitative immunoassay diagnostics. The photometer quantifies the concentration of antigens based on light absorption, which allows for a low-cost implementation without expensive optical components. We propose a light controller to lower the start-up and settling time of the light source to 30 seconds, to facilitate fast measurement starts, and to decrease the overall measurement times. The application-specific integrated circuit (ASIC) contains a 6 x 7-sensor array with 100 m x 100 m photodiodes that serve as signal transducers. The ASIC was developed in a normal 0.35-m CMOS technology, avoiding the need for expensive post-CMOS processes. We present our strategy for the assembly of the ASIC and the immobilization of antibodies. For its first time, we demonstrate the quantification of prostate specific antigen (PSA) with an optoelectronic CMOS biochip using this approach. A PSA immunoassay is performed on the top surface of the CMOS sensor array, enzyme kinetics and PSA concentration are measured within 6 minutes with a limit of detection (LoD) of 0.5 ng/ml, which meets clinical testing requirements. We achieve an overall coefficient of variation (CV) of 7%, which is good compared to other point-of-care (PoC) systems.In this paper, a fully integrated active rectifier with triple feedback loops is proposed to enhance power conversion efficiency (PCE) over a wide loading range by calibrating both the gate transition timing and power switch size. The on- and off-transitions of the power switches are calibrated using a hybrid delay-based gate control circuit (HDGCC) with hybrid feedback loops. Conventional active rectifiers that only focused on calibrating the gate transition timing of a NMOS power switch with a fixed power switch size exhibit a low PCE when the loading condition deviates from the predetermined range. Thus, an automatic size selector based on a third feedback loop is proposed, which changes the power switch size based on the loading condition and ensures a stable operation of the hybrid loops by maintaining the voltage drop across the NMOS switches. An active rectifier was fabricated using the standard 0.18 m CMOS process. The effectiveness and robustness of the two-dimensional calibration were verified through measurements under an AC input voltage ranging from 2.5 to 5.0 V and an output power ranging from 1.25 to 125 mW. The peak voltage conversion ratio and peak PCE were 97.6% and 95.0%, respectively, at RL = 500 .In recent years, cancer patients survival prediction holds important significance for worldwide health problems, and has gained many researchers attention in medical information communities. Cancer patients survival prediction can be seen the classification work which is a meaningful and challenging task. Nevertheless, research in this field is still limited. In this paper, we design a novel Multimodal Graph Neural Network (MGNN) framework for predicting cancer survival, which explores the features of real-world multimodal data such as gene expression, copy number alteration and clinical data in a unified framework. Specifically, we first construct the bipartite graphs between patients and multimodal data to explore the inherent relation. Subsequently, the embedding of each patient on different bipartite graphs is obtained with graph neural network. Finally, a multimodal fusion neural layer is proposed to fuse the medical features from different modality data. Comprehensive experiments have been conducted on real-world datasets, which demonstrate the superiority of our modal with significant improvements against state-of-the-arts. Furthermore, the proposed MGNN is validated to be more robust on other four cancer datasets.Recent advances in RNA-seq technology have made identification of expressed genes affordable, and thus boosting repaid development of transcriptomic studies. Transcriptome assembly, reconstructing all expressed transcripts from RNA-seq reads, is an essential step to understand genes, proteins, and cell functions. Transcriptome assembly remains a challenging problem due to complications in splicing variants, expression levels, uneven coverage and sequencing errors. Here, we formulate the transcriptome assembly problem as path extraction on splicing graphs (or assembly graphs), and propose a novel algorithm MultiTrans for path extraction using mixed integer linear programming. MultiTrans is able to take into consideration coverage constraints on vertices and edges, the number of paths and the paired-end information simultaneously. We benchmarked MultiTrans against two state-of-the-art transcriptome assemblers, TransLiG and rnaSPAdes. Experimental results show that MultiTrans generates more accurate transcripts compared to TransLiG (using the same splicing graphs) and rnaSPAdes (using the same assembly graphs). MultiTrans is freely available at https//github.com/jzbio/MultiTrans.
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