다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.

/pdf/breast-cancer-classification-from-ultrasound-images-using-2krct9jy.pdf

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

Various deep learning methods have been proposed to segment breast lesions from ultrasound images. However, similar intensity distributions, variable tumor morphologies and blurred boundaries present challenges for breast lesions segmentation, especially for malignant tumors with irregular shapes. Considering the complexity of ultrasound images, we develop an adaptive attention U-net (AAU-net) to segment breast lesions automatically and stably from ultrasound images. Specifically, we introduce a hybrid adaptive attention module (HAAM), which mainly consists of a channel self-attention block and a spatial self-attention block, to replace the traditional convolution operation. Compared with the conventional convolution operation, the design of the hybrid adaptive attention module can help us capture more features under different receptive fields. Different from existing attention mechanisms, the HAAM module can guide the network to adaptively select more robust representation in channel and space dimensions to cope with more complex breast lesions segmentation. Extensive experiments with several state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets show that our method has better performance on breast lesions segmentation. Furthermore, robustness analysis and external experiments demonstrate that our proposed AAU-net has better generalization performance in the breast lesion segmentation. Moreover, the HAAM module can be flexibly applied to existing network frameworks. The source code is available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/CGPxy/AAU-net</uri> . 

/pdf/aau-net-an-adaptive-attention-u-net-for-breast-lesions-2l0h7uc7.pdf

AAU-Net: An Adaptive Attention U-Net for Breast Lesions Segmentation in Ultrasound Images

With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net architectures are most widely utilized in biomedical image segmentation to address the automation in identification and detection of the target regions or sub-regions. In recent studies, U-Net based approaches have illustrated state-of-the-art performance in different applications for the development of computer-aided diagnosis systems for early diagnosis and treatment of diseases such as brain tumor, lung cancer, alzheimer, breast cancer, etc. This article contributes to present the success of these approaches by describing the U-Net framework, followed by the comprehensive analysis of the U-Net variants for different medical imaging or modalities such as magnetic resonance imaging, X-ray, computerized tomography/computerized axial tomography, ultrasound, positron emission tomography, etc. Besides, this article also highlights the contribution of U-Net based frameworks in the on-going pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19.

Modality specific U-Net variants for biomedical image segmentation: A survey

Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends

Breast cancer is one of the deadliest cancers that cause women death globally. Ultrasound imaging is one of the commonly used diagnostic tools for detection and classification of breast abnormalities. In the past decades, computer-aided diagnosis (CAD) systems have been developed to improve the accuracy of diagnosis made by radiologists. In particular, automatic breast ultrasound (BUS) image segmentation is a critical step for cancer diagnosis using CAD. However, accurate tumor segmentation is still a challenge as a result of various ultrasound artifacts. This paper developed a novel segmentation framework based on deep learning architecture u-net, for breast ultrasound imaging. U-net is a convolutional neural network architecture designed for biology image segmentation with limited training data. It was originally proposed for neuronal structure segmentation in microscopy images. In our work, we modified and improved the method for BUS image segmentation. On a database of 221 BUS images, we first applied pre-processing techniques including contrast enhancement and speckle reduction to improve the image quality. Then the u-net model was trained and tested through two-fold cross-validation. In order to increase the size of training set, data augmentation strategies including rotation and elastic deformation were applied. Finally, a post-processing step that removed noisy region(s) from the segmentation result finalized the whole method. The area error metrics, dice coefficient, and similarity rate were calculated to evaluate the performance on the testing sets. We compared our method with another two fully automatic segmentation methods on the same dataset. Our method outperformed the other two significantly with the dice coefficient = 0.825 and similarity rate = 0.698. Experiment results showed that the modified u-net method is more robust and accurate in breast tumor segmentation for ultrasound images.

Development of a Deep-Learning-Based Method for Breast Ultrasound Image Segmentation

This study explored the hidden biomedical information from knee MR images for osteoarthritis prediction. We have computed the Cartilage Damage Index (CDI) information from 36 informative locations on tibiofemoral compartment from 3D MR imaging reconstruction and used PCA analysis to process the feature set. The processed feature set and original raw feature set were severed as input to four machine learning methods (artificial neural network (ANN), support vector machine (SVM), random forest and naive Bayes) respectively. To examine the different effect of medial and lateral informative locations, we have divided the 36-dimensional feature set into 18-dimensional medial feature set and 18-dimensional lateral feature set and run the experiment on four classifiers separately. Experiment results showed that the medial feature set generated better prediction performance than the lateral feature set, while using the total 36-dimensional feature set generated the best. PCA analysis is helpful in feature space reduction and performance improvement. The best performance was achieved by ANN with area under the receiver operating characteristic (ROC) curve = 0.761 and F-measure = 0.714. Experiment results indicated that the informative locations on medial tibiofemoral compartment contain more valuable information than informative locations on lateral tibiofemoral compartment, for OA severity prediction. Therefore, to improve the design of the clinically used CDI, it could be considered to select more points from the medial tibiofemoral compartment while reduce the number of points selected from the lateral tibiofemoral compartment.

Knee osteoarthritis prediction on MR images using cartilage damage index and machine learning methods

Respiratory diseases have become a global health problem and may lead to acute lung injury (ALI) in severe cases. ALI progression is associated with complex pathological changes; however, there are currently no effective therapeutic drugs. Excessive activation and recruitment of immunocytes in the lungs and the release of large amounts of cytokines are considered the primary causes of ALI, but the cellular mechanisms involved remain unknown. Therefore, new therapeutic strategies need to be developed to control the inflammatory response and prevent the further aggravation of ALI.

Chlorogenic acid reduces inflammation by inhibiting the elevated expression of KAT2A to ameliorate lipopolysaccharide‐induced acute lung injury

This paper studied the changing pattern of knee cartilage using 3D knee magnetic resonance (MR) images over a 12-month period. As a pilot study, we focused on the medial tibia compartment of the knee joint. To quantify the thickness of cartilage in this compartment, we utilized two methods: one was measurement through manual segmentation of cartilage on each slice of the 3D MR sequence; the other was measurement through cartilage damage index (CDI), which quantified the thickness on a few informative locations on cartilage. We employed the artificial neural networks (ANNs) to model the changing pattern of cartilage thickness. The input feature space was composed of the thickness information at a cartilage location as well as its neighborhood from baseline year data. The output categories were ‘changed’ and ‘no-change’, based on the thickness difference at the same location between the baseline year and the 12-month follow-up data. Different ANN models were trained by using CDI features and manual segmentation features. Further, for each type of feature, individual models were trained at different subregions of the medial tibia compartment, i.e., the bottom part, the middle part, the upper part, and the whole. Based on the experiment results, we found that CDI features generated better prediction performance than manual segmentation, on both whole medial tibia compartment and any subregion. For CDI, the best performance in term of AUC was obtained using the central CDI locations (AUC = 0.766), while the best performance for manual segmentation was obtained using all slices of the 3D MR sequence (AUC = 0.656). As experiment results showed, the CDI method demonstrated a stronger pattern of cartilage change than the manual segmentation method, which required up to 6-hour manual delineation of all MRI slices. The result should be further validated by extending the experiment to other compartments.

Identification of Knee Cartilage Changing Pattern

Big data provides us the source to extract new insights in various disciplines. In healthcare, big data may help discover the pathology of a disease and new effective treatments. However, the era of big data taxes the ability of many researchers to analyze and interact with data in biomedical research. Traditional statistical methods have limitations to infer relevant features for possibly complex data. Topological Data Analysis (TDA) provides a set of new topological and geometric tools to discover the hidden relations or key features from complex data. It may help identify the key risk factors related to a given disease and reduce the noise impact from other factors. In this paper, we use TDA to analyze the multidimensional data from MRI of knee osteoarthritis patients.

Topological Data Analysis on Magnetic Resonance Image Biomarkers

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