Calcification detection for intravascular ultrasound image using direct acyclic graph architecture: pre-trained model for 1-channel image

Coronary artery calcification is a calcium buildup within the walls of the arteries. It is considered a predominant marker for coronary artery disease. Thus many approaches have been developed for the automatic detection of calcification. The previous calcification detection was on segmentation of other structures as pre-processing steps or using the fact that the calcification often appears as a bright region. In this paper, an automated system proposed using a deep learning approach to detect the calcification absence and calcification presence in coronary artery IVUS image. A useful advantage of deep learning, compared to other methods is, it uses representations and features directly from the raw data, bypassing the need to manually extract features, a common that required in the traditional machine learning framework. The type of deep learning architecture used is 27 layers of convolutional neural networks (CNNs) using Direct Acyclic Graph. The proposed system used 2175 images and achieved an accuracy of 98.16% for Cartesian coordinate images and 99.08% for Polar Reconstructed Coordinate images.

Toward a Mixed Tangle-Blockchain Architecture

This paper presents the design and implementation of a new platform that takes into consideration the requirements and constraints resulting from the industrial context based on IoT. This platform combines the “Tangle” and “Blockchain” techniques. Tangle is primarily designed to address scale-up issues and the relatively high cost (time and resource) of transactions in a traditional blockchain-based platform. Unlike the “blockchain” structure, it consists of a solid mathematical foundation called DAG (Direct Acyclic Graph). It uses a validation process in which transactions are entered into the distributed registry after authenticating two other randomly selected transactions according to a Poisson distribution (thus, the locations of the new transactions are chosen using random runs in the graph). Therefore, it is an easily scalable system that does not require mining or transaction fees. We aim to study the integration of Tangle and Blockchain techniques to improve the performance and scalability of distributed registry-based platforms to be adapted in industrial enterprises whose processes incorporate or are based on IoT.

Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in US images

Background Outlining lesion contours in Ultra Sound (US) breast images is an important step in breast cancer diagnosis. Malignant lesions infiltrate the surrounding tissue, generating irregular contours, with spiculation and angulated margins, whereas benign lesions produce contours with a smooth outline and elliptical shape. In breast imaging, the majority of the existing publications in the literature focus on using Convolutional Neural Networks (CNNs) for segmentation and classification of lesions in mammographic images. In this study our main objective is to assess the ability of CNNs in detecting contour irregularities in breast lesions in US images. Methods In this study we compare the performance of two CNNs with Direct Acyclic Graph (DAG) architecture and one CNN with a series architecture for breast lesion segmentation in US images. DAG and series architectures are both feedforward networks. The difference is that a DAG architecture could have more than one path between the first layer and end layer, whereas a series architecture has only one path from the beginning layer to the end layer. The CNN architectures were evaluated with two datasets. Results With the more complex DAG architecture, the following mean values were obtained for the metrics used to evaluate the segmented contours: global accuracy: 0.956; IOU: 0.876; F measure: 68.77%; Dice coefficient: 0.892. Conclusion The CNN DAG architecture shows the best metric values used for quantitatively evaluating the segmented contours compared with the gold-standard contours. The segmented contours obtained with this architecture also have more details and irregularities, like the gold-standard contours.