Showing papers in "Computer Methods and Programs in Biomedicine in 2021"

TL;DR: Support for 2D, 3D and 4D images such as X-ray, histopathology, CT, ultrasound and diffusion MRI and focus on reproducibility and traceability to encourage open-science practices.

TL;DR: In this article, the authors investigated the bimodal spread of COVID-19 with hybrid model based on nonlinear autoregressive with radial base function (NAR-RBFs) neural network for SITR model.

TL;DR: An end to end computer-aided diagnosis system based on You Only Look Once that can detect most of the challenging cases of masses and classify them correctly and augmenting the training set only is the fairest and accurate to be applied in the realistic scenarios.

TL;DR: The proposed VSSC Net that segments blood vessels from both the retinal fundus images and coronary angiogram can be used for the early diagnosis of vessel disorders and could aid the physician to analyze the blood vessel structure of images obtained from multiple imaging sources.

TL;DR: A new multi-view clustering model called Consensus Multi-view Clustering (CMC) based on nonnegative matrix factorization for predicting the multiple stages of AD progression is proposed and can improve the predication performance of AD, but also can screen and classify the symptoms of different AD's phases.

TL;DR: The experimental results have proved that the method of detecting lung nodules based on Faster R-CNN algorithm has good accuracy and therefore, presents potential clinical value in lung disease diagnosis.

TL;DR: In this article, a combination of convolutional neural networks and recurrent neural networks (RNNs) based on Bi-directional long short short-term memory (BiLSTM) was used to detect VHD through phonocardiography (PCG) recordings.

TL;DR: OpenCARP as discussed by the authors is a Python-based simulator for cardiac electrophysiology, which allows developing and sharing simulation pipelines which automate in silico experiments including all modeling and simulation steps to increase reproducibility and productivity.

TL;DR: A comprehensive overview of the primary uses of IoT in healthcare, using the Systematic Literature Review (SLR) method, to analyze and comparison articles published in this field between 2015 and March 2020 and highlights the most critical challenges and case studies.

TL;DR: The PSPNet network reduces manual interaction in diagnosis, reduces dependence on medical personnel, improves the efficiency of disease diagnosis, and provides auxiliary strategies for subsequent medical diagnosis systems based on cardiac coronary angiography.

TL;DR: The improved 3D AlexNet can automatically complete the structured segmentation of prostate magnetic resonance images and is superior in terms of training time and parameter amount, or network performance evaluation.

TL;DR: A deep neural network-based classifier for the computer-aided classification of Diabetic Macular Edema, drusen, Choroidal NeoVascularization, and CNV from normal OCT images of the retina is proposed using a deep convolutional neural network having six Convolutional blocks.

TL;DR: A novel cervical cell classification method based on Graph Convolutional Network (GCN) that can achieve the better classification performance and also can be potentially used in automatic screening system of cervical cytology.

TL;DR: The statistical results prove the robustness of the WAVEn algorithm in reliably discriminating the CAD patients from healthy ones with high precision, and therefore it can be used for developing a decision support system for diagnosing CAD at an early stage.

TL;DR: In this article, an automated classification system was developed to classify ADHD, conduct disorder and ADHD+CD using EEG signals, which achieved the highest accuracy of 97.88% with the KNN classifier.

TL;DR: The proposed novel level set method is faster and more accurate than other state-of-the-art segmentation methods and shows satisfactory results for Glioma brain tumor segmentation due to superpixel fuzzy clustering accurate segmentation results.

TL;DR: The proposed approach is based on analysis over the whole brain by looking for decreases of tissue thickness, with the consequence of finding other regions of interest such as the cortex, which is indicative of Parkinson's disease.

TL;DR: In this paper, a modified U-Net based framework is presented, which leverages techniques from Squeeze-and-Excitation (SE) block, Atrous Spatial Pyramid Pooling (ASPP) and residual learning for accurate and robust liver Computed Tomography (CT) segmentation, and the effectiveness of the proposed method was tested on two public datasets LiTS17 and SLiver07.

TL;DR: In this paper, a comprehensive review on the two mental disorders: Major depressive disorder (MDD) and Bipolar disorder (BD) with noteworthy publications during the last ten years is presented, focusing on the literature works adopting neural networks fed by EEG signals.

TL;DR: In this paper, the authors proposed a meta-validation method to assess the soundness of EV procedures by considering both dataset cardinality and the similarity of the EV dataset with respect to the training set.

TL;DR: In this article, a non-local convolutional block attention module (NCBAM) is proposed to automatically classify ECG heartbeats, which can capture long-range dependencies of representative features along spatial and channel axis.

TL;DR: In this article, a semi-supervised GAN model was developed to augment the breast ultrasound images, which were subsequently used to classify breast masses using a convolutional neural network (CNN).

TL;DR: In this article, an automated system for classifying arrhythmias using single-lead ECG signals is proposed, which uses a combination of three groups of features: RR intervals, signal morphology, and higher-order statistics.

TL;DR: Wang et al. as discussed by the authors used the depthwise separable convolution (DSC) to replace the conventional convolution, which reduced the parameters and computational costs of the proposed neural network significantly compared with conventional neural networks.

TL;DR: In this paper, a deep learning-based system was developed to predict the diagnosis of acute leukaemia using blood cell images using a set of 731 blood smears containing 16,450 single-cell images from 100 healthy controls, 191 patients with viral infections and 148 with acute leukemia.

TL;DR: In this paper, a novel methodology for robust vessel segmentation is proposed, handling the existing challenges presented in the literature, which consists of three stages, pre-processing, main processing, and post-processing.

TL;DR: The proposed EKF-SVM based method has better classification performance for positive brain tumor images, which was mainly due to the dearth of negative examples in the dataset.

TL;DR: A structured hierarchical segmentation method is presented that combines the advantages of two deep-learning methods and achieves accurate and robust identification of each lumbar vertebra and fine segmentation of individual vertebra.

TL;DR: An automated classification algorithm could recognize and localize coronary artery stenosis highly accurately and might provide the basis for a screening and assistant tool for the interpretation of coronary angiography.

TL;DR: A novel Convolutional Neural Network for nodule segmentation that combines a light and efficient architecture with innovative loss function and segmentation strategy and proved that the behaviour of the algorithm is very near to the human performance and its segmentation masks are almost indistinguishable from the ones made by the radiologists.