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Background: Structural magnetic resonance imaging (sMRI) can provide morphological information about the structure and function of the brain in the same scanning process. It has been widely used in the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Purpose: To capture the anatomical changes in the brain caused by AD/MCI, deep learning‐based MRI image analysis methods have been proposed in recent years. However, it is observed that the performance of most existing methods is limited as they only construct a single type of deep network and ignore the significance of other clinical information. Methods: To make up for these defects, an ensemble framework that incorporates three types of dedicatedly‐designed convolutional neural networks (CNNs) and a multilayer perceptron (MLP) network is proposed, where three CNNs with entropy‐based multi‐instance learning pooling layers have more reliable feature selection abilities. The dedicatedly‐designed base classifiers can make use of the heterogeneous data, and empower the framework with enhanced diversity and robustness. In particular, to consider the interactions among the base classifiers, a novel multi‐head self‐attention voting scheme is designed. Moreover, considering the chance that MCI can be transformed to AD, the proposed framework is designed to diagnose AD and predict MCI conversion simultaneously, with the aid of the transfer learning technique. Results: For performance evaluation and comparison, extensive experiments are conducted on the public dataset of the Alzheimer's Disease Neuroimaging Initiative (ADNI). The results show that the proposed ensemble framework provides superior performance under most of the evaluation metrics. Especially, the proposed framework achieves state‐of‐the‐art diagnostic accuracy (98.61% for the AD diagnosis task, and 84.49% for the MCI conversion prediction task). Conclusions: These promising results demonstrate the proposed ensemble framework can accurately diagnose AD patients and predict the conversion of MCI patients, which has the potential of clinical practice for diagnosing AD and MCI. [ABSTRACT FROM AUTHOR]

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]