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Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future. [ABSTRACT FROM AUTHOR]

Despite notable advancements achieved on Hyperspectral (HS) pansharpening tasks through deep learning techniques, previous methods are inherently constrained by convolution or self-attention intrinsic defects, leading to limited performance. In this paper, we proposed an Attention-Interactive Dual-Branch Convolutional Neural Network (AIDB-Net) for HS pansharpening. Our model purely consists of convolutional layers and simultaneously inherits the strengths of both convolution and self-attention, especially the modeling of short- and long-range dependencies. Specially, we first extract, tokenize, and align the hyperspectral image (HSI) and panchromatic image (PAN) by Overlapping Patch Embedding Blocks. Then, we specialize a novel Spectral-Spatial Interactive Attention which is able to globally interact and fuse the cross-modality features. The resultant token-global similarity scores can guide the refinement and renewal of the textural details and spectral characteristics within HSI features. By deeply combined these two paradigms, our AIDB-Net significantly improve the pansharpening performance. Moreover, with the acceleration by the convolution inductive bias, our interactive attention can be trained without large scale dataset and achieves competitive time cost with its counterparts. Compared with the state-of-the-art methods, our AIDB-Net makes 5.2%, 3.1%, and 2.2% improvement on PSNR metric on three public datasets, respectively. Comprehensive experiments quantitatively and qualitatively demonstrate the effectiveness and superiority of our AIDB-Net. [ABSTRACT FROM AUTHOR]

With the advancement of micro- and nanomanufacturing technologies, electronic components and chips are increasingly being miniaturized. To automatically identify their packaging materials for ensuring the reliability of ICs, a hybrid deep learning framework termed as CNN–transformer interaction (CTI) model is designed on IC packaging images in this paper, in which several cascaded CTI blocks are designed to bidirectionally capture local and global features from the IC packaging image. Each CTI block involves a CNN branch with two designed convolutional neural networks (CNNs) for CNN local features and a transformer branch with two transformers for transformer global features and transformer local-window features. A bidirectional interaction mechanism is designed to interactively transfer the features in channel and spatial dimensions between the CNNs and transformers. Experimental results indicate that the hybrid framework can recognize three types of IC packaging materials with a good performance of 96.16% F1-score and 97.92% accuracy, which is superior to some existing deep learning methods. [ABSTRACT FROM AUTHOR]

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed. [ABSTRACT FROM AUTHOR]

In recent years, there has been significant progress in human pose estimation, fueled by the widespread adoption of deep convolutional neural networks. However, despite these advancements, multi-person 2D pose estimation still remains highly challenging due to factors such as occlusion, noise, and non-rigid body movements. Currently, most multi-person pose estimation approaches handle joint localization and association separately. This study proposes a direct regression-based method to estimate the 2D human pose from a single image. The authors name this network YOLO-Rlepose. Compared to traditional methods, YOLO-Rlepose leverages Transformer models to better capture global dependencies between image feature blocks and preserves sufficient spatial information for keypoint detection through a multi-head self-attention mechanism. To further improve the accuracy of the YOLO-Rlepose model, this paper proposes the following enhancements. Firstly, this study introduces the C3 Module with Swin Transformer (C3STR). This module builds upon the C3 module in You Only Look Once (YOLO) by incorporating a Swin Transformer branch, enhancing the YOLO-Rlepose model's ability to capture global information and rich contextual information. Next, a novel loss function named Rle-Oks loss is proposed. The loss function facilitates the training process by learning the distributional changes through Residual Log-likelihood Estimation. To assign different weights based on the importance of different keypoints in the human body, this study introduces a weight coefficient into the loss function. The experiments proved the efficiency of the proposed YOLO-Rlepose model. On the COCO dataset, the model outperforms the previous SOTA method by 2.11% in AP. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurate tumor localization is essential for effective clinical diagnosis and treatment. However, traditional magnetic particle imaging (MPI) algorithms struggle to precisely locate tumors, resulting in challenges when quantifying them. This study aims to address the issue of precise tumor localization in MPI through the application of a deep learning approach. By integrating Convolutional Neural Network (CNN) and Transformer modules, the goal is to improve image quality and enhance the accuracy of tumor quantification in MPI images. The research utilizes a combination of CNN and Transformer modules to capture both global and local features within MPI images. Through the application of deep learning techniques, the study seeks to remove blurry artifacts from reconstructed images, ultimately may help improve the precision of tumor localization and quantification in MPI. This approach holds significant potential for advancing MPI technology and introducing novel methodologies for future medical imaging research. Additionally, the validation of transfer learning on authentic MPI images may enhance the overall accuracy and reliability of MPI image reconstruction. Background: Magnetic Particle Imaging (MPI) is an emerging molecular imaging technique. However, since X-space reconstruction ignores system properties, it can lead to blurring of the reconstructed image, posing challenges for accurate quantification. To address this issue, we propose the use of deep learning to remove the blurry artifacts; (2) Methods: Our network architecture consists of a combination of Convolutional Neural Network (CNN) and Transformer. The CNN utilizes convolutional layers to automatically extract pixel-level local features and reduces the size of feature maps through pooling layers, effectively capturing local information within the images. The Transformer module is responsible for extracting contextual features from the images and efficiently capturing long-range dependencies, enabling a more effective modeling of global features in the images. By combining the features extracted by both CNN and Transformer, we capture both global and local features simultaneously, thereby improving the quality of reconstructed images; (3) Results: Experimental results demonstrate that the network effectively removes blurry artifacts from the images, and it exhibits high accuracy in precise tumor quantification. The proposed method shows superior performance over the state-of-the-art methods; (4) Conclusions: This bears significant implications for the image quality improvement and clinical application of MPI technology. [ABSTRACT FROM AUTHOR]