BMCS-Net: A Bi-directional multi-scale cascaded segmentation network based on transformer-guided feature Aggregation for medical images.

convolutional neural networks (CNNs) show great potential in medical image segmentation tasks, and can provide reliable basis for disease diagnosis and clinical research. However, CNNs exhibit general limitations on modeling explicit long-range relation, and existing cures, resorting to building deep encoders along with aggressive downsampling operations, leads to loss of localized details. Transformer has naturally excellent ability to model the global features and long-range correlations of the input information, which is strongly complementary to the inductive bias of CNNs.

HyperTDP-Net: A Hyper-densely Connected Compression-and-Decomposition Network Based on Trident Dilated Perception for PET and MRI Image Fusion.

. Since medical images generated by medical devices have low spatial resolution and quality, fusion approaches on medical images can generate a fused image containing a more comprehensive range of different modal features to help physicians accurately diagnose diseases. Conventional methods based on deep learning for medical image fusion usually extract only local features without considering their global features, which often leads to the problem of unclear detail information in the final fused image.

PET and MRI image fusion based on a dense convolutional network with dual attention.

The fusion techniques of different modalities in medical images, e.g., Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), are increasingly significant in many clinical applications by integrating the complementary information from different medical images.

An Efficient Frequency Estimator for a Complex Exponential Signal Based on Interpolation of Selectable DTFT Samples.

The frequency estimation of complex exponential carrier signals in noise is a critical problem in signal processing. To solve this problem, a new iterative frequency estimator is presented in this paper. By iteratively computing the interpolation of DTFT samples, the proposed algorithm obtains a fine frequency estimate.

SeedSortNet: a rapid and highly effificient lightweight CNN based on visual attention for seed sorting.

Seed purity directly affects the quality of seed breeding and subsequent processing products. Seed sorting based on machine vision provides an effective solution to this problem. The deep learning technology, particularly convolutional neural networks (CNNs), have exhibited impressive performance in image recognition and classification, and have been proven applicable in seed sorting.

An Intelligent Heartbeat Classification System Based on Attributable Features with AdaBoost+Random Forest Algorithm.

Arrhythmia is a common cardiovascular disease that can threaten human life. In order to assist doctors in accurately diagnosing arrhythmia, an intelligent heartbeat classification system based on the selected optimal feature sets and AdaBoost + Random Forest model is developed. This system can acquire ECG signals through the Holter and transmit them to the cloud platform for preprocessing and feature extraction, and the features are input into AdaBoost + Random Forest for heartbeat classification.

Heartbeat Classification Based on Multifeature Combination and Stacking-DWKNN Algorithm.

Arrhythmia is one of the most common abnormal symptoms that can threaten human life. In order to distinguish arrhythmia more accurately, the classification strategy of the multifeature combination and Stacking-DWKNN algorithm is proposed in this paper. The method consists of four modules.

Nonrigid Medical Image Registration Using an Information Theoretic Measure Based on Arimoto Entropy with Gradient Distributions.

This paper introduces a new nonrigid registration approach for medical images applying an information theoretic measure based on Arimoto entropy with gradient distributions. A normalized dissimilarity measure based on Arimoto entropy is presented, which is employed to measure the independence between two images. In addition, a regularization term is integrated into the cost function to obtain the smooth elastic deformation.

3D nonrigid medical image registration using a new information theoretic measure.

This work presents a novel method for the nonrigid registration of medical images based on the Arimoto entropy, a generalization of the Shannon entropy. The proposed method employed the Jensen-Arimoto divergence measure as a similarity metric to measure the statistical dependence between medical images. Free-form deformations were adopted as the transformation model and the Parzen window estimation was applied to compute the probability distributions.

Gamma regularization based reconstruction for low dose CT.

Reducing the radiation in computerized tomography is today a major concern in radiology. Low dose computerized tomography (LDCT) offers a sound way to deal with this problem. However, more severe noise in the reconstructed CT images is observed under low dose scan protocols (e.