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The problem of missing or unavailable magnetic resonance imaging modalities challenges clinical diagnosis and medical image analysis technology. Although the development of deep learning and the proposal of large models have improved medical analytics, this problem still needs to be better resolved.The purpose of this study was to efficiently adapt the Segment Anything Model, a two‐dimensional visual foundation model trained on natural images, to address the challenge of brain tumor segmentation with missing modalities. We designed a twin network structure that processes missing and intact magnetic resonance imaging (MRI) modalities separately using shared parameters. It involved comparing the features of two network branches to minimize differences between the feature maps derived from them. We added a multimodal adapter before the image encoder and a spatial–depth adapter before the mask decoder to fine‐tune the Segment Anything Model for brain tumor segmentation. The proposed method was evaluated using datasets provided by the MICCAI BraTS2021 Challenge. In terms of accuracy and robustness, the proposed method is better than existing solutions. The proposed method can segment brain tumors well under the missing modality condition. [ABSTRACT FROM AUTHOR]

Deep learning methods, with their good performance in semantic representation of different images, have been widely used for saliency detection. Recent saliency detection methods have applied deep learning to obtain high-level features and combined them with hand-crafted low-level features to estimate saliency in the images. However, it is difficult to find the relationship between high-level and low-level features, resulting in incomplete integration framework for saliency detection. In this paper, we novely propose a saliency detection model by integrating high-level and low-level features with joint probability estimation. Firstly, the high-level features from FCN-8S network are used to estimate the probability of each superpixel as foreground or background region. Secondly, low-level features are extracted from each superpixels and clustered via affinity propagation (AP) clustering. The distributions of vectors from different clusters are consequently utilized to calculate the conditional probability of each superpixel as salient object under different assumptions. Thirdly, the joint probability of each superpixel as salient object in foreground or background is computed to compose the saliency map of the whole image. To further improve the uniformity of saliency in the same object region, the structured random forest (SRF) method is used to detect the contour of the image and the saliency of superpixels in homogeneous regions are uniformly merged. The advantage of high-level features in representing semantic regions and that of low-level features in differentiating local details in the image are unified and restrained by the joint probability estimation in the proposed model. Experimental results demonstrate that the proposed method provide better saliency detection performance than the state-of-the-art methods on 5 public databases. [ABSTRACT FROM AUTHOR]

The industrial Internet connects equipment to the network and utilizes the data generated to assist businesses. Industrial big data is the result of data accumulation; thus, the industrial Internet has to adopt new technologies—namely, software-defined industrial networks (SDIN) —to keep up with these developments. This study suggests a deep differential privacy data protection algorithm based on SDIN. The deep learning model is analyzed and integrated with differential privacy to provide the process framework for the deep differential privacy data protection algorithm. The equivalent model of the generative adversarial network is used to allow the attacker to obtain the optimal fake samples. The balance between dataset availability and privacy protection is achieved by implementing parameter tuning on the deep differential privacy model. The experimental results show that the proposed algorithm has strong industrial data privacy protection and high data availability and can effectively guarantee the privacy security of industrial data. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19) has spread globally since the first case was reported in December 2019, becoming a world-wide existential health crisis with over 90 million total confirmed cases. Segmentation of lung infection from computed tomography (CT) scans via deep learning method has a great potential in assisting the diagnosis and healthcare for COVID-19. However, current deep learning methods for segmenting infection regions from lung CT images suffer from three problems: (1) Low differentiation of semantic features between the COVID-19 infection regions, other pneumonia regions and normal lung tissues; (2) High variation of visual characteristics between different COVID-19 cases or stages; (3) High difficulty in constraining the irregular boundaries of the COVID-19 infection regions. To solve these problems, a multi-input directional UNet (MID-UNet) is proposed to segment COVID-19 infections in lung CT images. For the input part of the network, we firstly propose an image blurry descriptor to reflect the texture characteristic of the infections. Then the original CT image, the image enhanced by the adaptive histogram equalization, the image filtered by the non-local means filter and the blurry feature map are adopted together as the input of the proposed network. For the structure of the network, we propose the directional convolution block (DCB) which consist of 4 directional convolution kernels. DCBs are applied on the short-cut connections to refine the extracted features before they are transferred to the de-convolution parts. Furthermore, we propose a contour loss based on local curvature histogram then combine it with the binary cross entropy (BCE) loss and the intersection over union (IOU) loss for better segmentation boundary constraint. Experimental results on the COVID-19-CT-Seg dataset demonstrate that our proposed MID-UNet provides superior performance over the state-of-the-art methods on segmenting COVID-19 infections from CT images. • We propose a multidimensional input to reflect the GGOs or infiltration features. • We propose a directional convolution block to represent the fibrotic-streak features. • We propose a novel region contour loss function to restric irregular boundaries. [ABSTRACT FROM AUTHOR]

Recently, deep-learning based face super-resolution methods have succeeded in hallucinating high-resolution face images from low-resolution inputs. However, since the given face images have tiny resolution and arbitrary characteristics which need to be reconstructed at high magnification factors, existing methods still suffer from large space of possible mapping functions, resulting in the limited performance in producing sharp textures. In this paper, we propose a novel CNN-based Dual Closed-loop Network (DCLNet) to minimize the possible mapping space. To that end, we design two dual learning networks to form the dual closed-loop structure with a primary face super-resolution network, which can provide the primary branch with additional prior constraint to guide the essential facial features restoring. Our work represents the first attempt to introduce multiple dual learning networks into face super-resolution model to constrain the possible mapping space. In addition, a progressive facial prior estimation framework and a new prior-guided feature enhancement module are presented to integrate the facial prior knowledge and guide the face image super-resolution. By generating multiple facial components maps for the activation of essential facial parts, our enhancement module can address the difficulty in learning and integrating strong priors into a face super-resolution model. In this way, the collaboration between the face super-resolution and alignment processes can be enhanced with effect. Extensive experiments are implemented on CelebA and Helen dataset, showing that our proposed method can provide state-of-the-art or even better performance in both quantitative and qualitative measurements. [ABSTRACT FROM AUTHOR]