MH2AFormer: An Efficient Multiscale Hierarchical Hybrid Attention with a Transformer for Bladder Wall and Tumor Segmentation.

Achieving accurate bladder wall and tumor segmentation from MRI is critical for diagnosing and treating bladder cancer. However, automated segmentation remains challenging due to factors such as comparable density distributions, intricate tumor morphologies, and unclear boundaries. Considering the attributes of bladder MRI images, we propose an efficient multiscale hierarchical hybrid attention with a transformer (MH2AFormer) for bladder cancer and wall segmentation. Specifically, a multiscale hybrid attention and transformer (MHAT) module in the encoder is designed to adaptively extract and aggregate multiscale hybrid feature representations from the input image. In the decoder stage, we devise a multiscale hybrid attention (MHA) module to generate high-quality segmentation results from multiscale hybrid features. Combining these modules enhances the feature representation and guides the model to focus on tumor and wall regions, which helps to solve bladder image segmentation challenges. Moreover, MHAT utilizes the Fast Fourier Transformer with a large kernel (e.g., 224*******224) to model global feature relationships while reducing computational complexity in the encoding stage. The model performance was evaluated on two datasets. As a result, the model achieves relatively best results regarding the intersection over union (IoU) and dice similarity coefficient (DSC) on both datasets (Dataset A: IoU=80.26%, DSC=88.20%; Dataset B: IoU=89.74%, DSC=94.48%). These advantageous outcomes substantiate the practical utility of our approach, highlighting its potential to alleviate the workload of radiologists when applied in clinical settings.

DBPNDNet: dual-branch networks using 3DCNN toward pulmonary nodule detection.

With the advancement of artificial intelligence, CNNs have been successfully introduced into the discipline of medical data analyzing. Clinically, automatic pulmonary nodules detection remains an intractable issue since those nodules existing in the lung parenchyma or on the chest wall are tough to be visually distinguished from shadows, background noises, blood vessels, and bones. Thus, when making medical diagnosis, clinical doctors need to first pay attention to the intensity cue and contour characteristic of pulmonary nodules, so as to locate the specific spatial locations of nodules. To automate the detection process, we propose an efficient architecture of multi-task and dual-branch 3D convolution neural networks, called DBPNDNet, for automatic pulmonary nodule detection and segmentation. Among the dual-branch structure, one branch is designed for candidate region extraction of pulmonary nodule detection, while the other incorporated branch is exploited for lesion region semantic segmentation of pulmonary nodules. In addition, we develop a 3D attention weighted feature fusion module according to the doctor's diagnosis perspective, so that the captured information obtained by the designed segmentation branch can further promote the effect of the adopted detection branch mutually. The experiment has been implemented and assessed on the commonly used dataset for medical image analysis to evaluate our designed framework. On average, our framework achieved a sensitivity of 91.33% false positives per CT scan and reached 97.14% sensitivity with 8 FPs per scan. The results of the experiments indicate that our framework outperforms other mainstream approaches.

SAMS-Net: Fusion of attention mechanism and multi-scale features network for tumor infiltrating lymphocytes segmentation.

Automatic segmentation of tumor-infiltrating lymphocytes (TILs) from pathological images is essential for the prognosis and treatment of cancer. Deep learning technology has achieved great success in the segmentation task. It is still a challenge to realize accurate segmentation of TILs due to the phenomenon of blurred edges and adhesion of cells. To alleviate these problems, a squeeze-and-attention and multi-scale feature fusion network (SAMS-Net) based on codec structure, namely SAMS-Net, is proposed for the segmentation of TILs. Specifically, SAMS-Net utilizes the squeeze-and-attention module with the residual structure to fuse local and global context features and boost the spatial relevance of TILs images. Besides, a multi-scale feature fusion module is designed to capture TILs with large size differences by combining context information. The residual structure module integrates feature maps from different resolutions to strengthen the spatial resolution and offset the loss of spatial details. SAMS-Net is evaluated on the public TILs dataset and achieved dice similarity coefficient (DSC) of 87.2% and Intersection of Union (IoU) of 77.5%, which improved by 2.5% and 3.8% compared with UNet. These results demonstrate the great potential of SAMS-Net in TILs analysis and can further provide important evidence for the prognosis and treatment of cancer.

The global mRNA vaccine patent landscape.

In light of their quick development and low risk, mRNA vaccines are gradually replacing traditional vaccines. In order to characterize the patent landscape of mRNA vaccines, this study collated mRNA vaccine-related applications that have been registered since 1962. Accordingly, the 1852 patent families were discussed in relation to their temporal distribution, geographic scope, organizational assignees, and co-patenting activities. mRNA vaccines were shown to demonstrate promise in infectious disease, cancer immunotherapy, and allergic disease, with a focus on lipid nanoparticles. Notably, these vaccines are being developed against a backdrop of fierce industrial competition and intensive collaboration with a rise in applications. The findings of this study highlighted cutting-edge inventions, key players, and collaboration dynamics among institutions. By understanding the landscape of mRNA vaccine patents, researchers and those in industry may better comprehend the latest trends in this area, which may also assist relevant decision-making by academics, government officials, and industrial leaders.

Multi-scale brain tumor segmentation combined with deep supervision.

PURPOSE: Fully convolutional neural networks (FCNNs) have achieved good performance in the field of medical image segmentation. FCNNs that use multimodal images and multi-scale feature extraction have higher accuracy for brain tumor segmentation. Therefore, we have made some improvements to U-Net for fully automated segmentation of gliomas using multimodal images. And we named it multi-scale dilate network with deep supervision (MSD-Net). METHODS: MSD-Net is a symmetrical structure composed of a down-sampling process and an up-sampling process. In the down-sampling process, we use the multi-scale feature extraction block (ME) to extract multi-scale features and focus on primary features. Unlike other methods, ME consists of dilate convolution and standard convolution. Dilate convolution extracts multi-scale informations and standard convolution merges features of different scales. Hence, the output of the ME contains local information and global information. During the up-sampling process, we add a deep supervision block (DSB), which can shorten the length of back-propagation. In this paper, we pay more attention to the importance of shallow features for feature restoration. RESULTS: Our network validated in the BraTS17's validation dataset. The DSC scores of MSD-Net for complete tumor, tumor core and enhancing tumor were 0.88, 0.81 and 0.78, respectively, which outperforms most networks. CONCLUSION: This study shows that ME enhances the feature extraction ability of the network and improves the accuracy of segmentation results. DSB speeds up the convergence of the network. In addition, we should also pay attention to the contribution of shallow features to feature restoration.

Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net.

Since the outbreak of COVID-19 in 2019, the rapid spread of the epidemic has brought huge challenges to medical institutions. If the pathological region in the COVID-19 CT image can be automatically segmented, it will help doctors quickly determine the patient's infection, thereby speeding up the diagnosis process. To be able to automatically segment the infected area, we proposed a new network structure and named QC-HC U-Net. First, we combine residual connection and dense connection to form a new connection method and apply it to the encoder and the decoder. Second, we choose to add Hypercolumns in the decoder section. Compared with the benchmark 3D U-Net, the improved network can effectively avoid vanishing gradient while extracting more features. To improve the situation of insufficient data, resampling and data enhancement methods are selected in this paper to expand the datasets. We used 63 cases of MSD lung tumor data for training and testing, continuously verified to ensure the training effect of this model, and then selected 20 cases of public COVID-19 data for training and testing. Experimental results showed that in the segmentation of COVID-19, the specificity and sensitivity were 85.3% and 83.6%, respectively, and in the segmentation of MSD lung tumors, the specificity and sensitivity were 81.45% and 80.93%, respectively, without any fitting.

SHA-MTL: soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification.

Purpose The automatic analysis of ultrasound images facilitates the diagnosis of breast cancer effectively and objectively. However, due to the characteristics of ultrasound images, it is still a challenging task to achieve analyzation automatically. We suppose that the algorithm will extract lesion regions and distinguish categories easily if it is guided to focus on the lesion regions.Method We propose a multi-task learning (SHA-MTL) model based on soft and hard attention mechanisms for breast ultrasound (BUS) image simultaneous segmentation and binary classification. The SHA-MTL model consists of a dense CNN encoder and an upsampling decoder, which are connected by attention-gated (AG) units with soft attention mechanism. Cross-validation experiments are performed on BUS datasets with category and mask labels, and multiple comprehensive analyses are performed on the two tasks.Results We assess the SHA-MTL model on a public BUS image dataset. For the segmentation task, the sensitivity and DICE of the SHA-MTL model to the lesion regions increased by 2.27% and 1.19% compared with the single task model, respectively. The classification accuracy and F1 score increased by 2.45% and 3.82%, respectively.Conclusion The results validate the effectiveness of our model and indicate that the SHA-MTL model requires less a priori knowledge to achieve better results by comparing with other recent models. Therefore, we can draw the conclusion that paying more attention to the lesion region of BUS is conducive to the discrimination of lesion types.

A deep semantic segmentation correction network for multi-model tiny lesion areas detection.

BACKGROUND: Semantic segmentation of white matter hyperintensities related to focal cerebral ischemia (FCI) and lacunar infarction (LACI) is of significant importance for the automatic screening of tiny cerebral lesions and early prevention of LACI. However, existing studies on brain magnetic resonance imaging lesion segmentation focus on large lesions with obvious features, such as glioma and acute cerebral infarction. Owing to the multi-model tiny lesion areas of FCI and LACI, reliable and precise segmentation and/or detection of these lesion areas is still a significant challenge task. METHODS: We propose a novel segmentation correction algorithm for estimating the lesion areas via segmentation and correction processes, in which we design two sub-models simultaneously: a segmentation network and a correction network. The segmentation network was first used to extract and segment diseased areas on T2 fluid-attenuated inversion recovery (FLAIR) images. Consequently, the correction network was used to classify these areas at the corresponding locations on T1 FLAIR images to distinguish between FCI and LACI. Finally, the results of the correction network were used to correct the segmentation results and achieve segmentation and recognition of the lesion areas. RESULTS: In our experiment on magnetic resonance images of 113 clinical patients, our method achieved a precision of 91.76% for detection and 92.89% for classification, indicating a powerful method to distinguish between small lesions, such as FCI and LACI. CONCLUSIONS: Overall, we developed a complete method for segmentation and detection of WMHs related to FCI and LACI. The experimental results show that it has potential clinical application potential. In the future, we will collect more clinical data and test more types of tiny lesions at the same time.

A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis.

Breast cancer has been one of the main diseases that threatens women's life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator R presents a new way to choose the base classifier for ensemble learning.

Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model.

Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). However, breast cancer multi-classification from histopathological images faces two main challenges from: (1) the great difficulties in breast cancer multi-classification methods contrasting with the classification of binary classes (benign and malignant), and (2) the subtle differences in multiple classes due to the broad variability of high-resolution image appearances, high coherency of cancerous cells, and extensive inhomogeneity of color distribution. Therefore, automated breast cancer multi-classification from histopathological images is of great clinical significance yet has never been explored. Existing works in literature only focus on the binary classification but do not support further breast cancer quantitative assessment. In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.

Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images.

Accurate registration of dynamic contrast-enhanced (DCE) MR breast images is challenging due to the temporal variations of image intensity and the non-rigidity of breast motion. The former can cause the well-known tumor shrinking/expanding problem in registration process while the latter complicates the task by requiring an estimation of non-rigid deformation. In this paper, we treat the intensity's temporal variations as "corruptions" which spatially distribute in a sparse pattern and model them with a L1 norm and a Lorentzian norm. We show that these new image similarity measurements can characterize the non-Gaussian property of the difference between the pre-contrast and post-contrast images and help to resolve the shrinking/expanding problem by forgiving significant image variations. Furthermore, we propose an iteratively re-weighted least squares based method and a linear programming based technique for optimizing the objective functions obtained using these two novel norms. We show that these optimization techniques outperform the traditional gradient-descent approach. Experimental results with sequential DCE-MR images from 28 patients show the superior performances of our algorithms.