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As one of the most common diabetic complications, diabetic retinopathy (DR) can cause retinal damage, vision loss and even blindness. Automated DR grading technology has important clinical significance, which can help ophthalmologists achieve rapid and early diagnosis. With the popularity of deep learning, DR grading based on the convolutional neural networks (CNNs) has become the mainstream method. Unfortunately, although the CNN-based method can achieve satisfactory diagnostic accuracy, it lacks significant clinical information. In this paper, a lesion-attention pyramid network (LAPN) is presented. The pyramid network integrates the subnetworks with different resolutions to get multi-scale features. In order to take the lesion regions in the high-resolution image as the diagnostic evidence, the low-resolution network calculates the lesion activation map (using the weakly-supervised localization method) and guides the high-resolution network to concentrate on the lesion regions. Furthermore, a lesion attention module (LAM) is designed to capture the complementary relationship between the high-resolution features and the low-resolution features, and to fuse the lesion activation map. Experiment results show that the proposed scheme outperforms other existing approaches, and the proposed method can provide lesion activation map with lesion consistency as an additional evidence for clinical diagnosis. [ABSTRACT FROM AUTHOR]

Chemical mechanical polishing (CMP) is one of the most powerful technologies to achieve global planarization for precision machining of the wafer surface. CMP contributes to intelligent manufacturing in Industry 4.0. Prediction of the material removal rate (MRR) is of vital significance for product quality control during the CMP process. There is no generally accepted theory to expound the principle of material removal in CMP. This paper proposes data-driven approaches to predict MRR based on deep learning methods to pursue better prediction performance. Random forest is employed to obtain the process variables which have a significant influence on MRR prediction and act as the input of the neural network. In addition, it is firstly proposed to predict MRR with the aid of residual convolutional neural network (ResCNN). The dataset provided by the PHM2016 Data Challenge is applied to compare the MRR prediction performance of the ResCNN with the CNN-based approach. Experimental results show that the prediction performance of ResCNN outperforms all the existing approaches reported in the literature. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19) still presents a pandemic trend globally. Detecting infected individuals and analyzing their status can provide patients with proper healthcare while protecting the normal population. Chest CT (computed tomography) is an effective tool for screening of COVID-19. It displays detailed pathology-related information. To achieve automated COVID-19 diagnosis and lung CT image segmentation, convolutional neural networks (CNNs) have become mainstream methods. However, most of the previous works consider automated diagnosis and image segmentation as two independent tasks, in which some focus on lung fields segmentation and the others focus on single-lesion segmentation. Moreover, lack of clinical explainability is a common problem for CNN-based methods. In such context, we develop a multi-task learning framework in which the diagnosis of COVID-19 and multi-lesion recognition (segmentation of CT images) are achieved simultaneously. The core of the proposed framework is an explainable multi-instance multi-task network. The network learns task-related features adaptively with learnable weights, and gives explicable diagnosis results by suggesting local CT images with lesions as additional evidence. Then, severity assessment of COVID-19 and lesion quantification are performed to analyze patient status. Extensive experimental results on real-world datasets show that the proposed framework outperforms all the compared approaches for COVID-19 diagnosis and multi-lesion segmentation. [ABSTRACT FROM AUTHOR]