FCFDiff-Net: full-conditional feature diffusion embedded network for 3D brain tumor segmentation.

Background: Brain tumor segmentation (BraTS) plays a critical role in medical imaging for early diagnosis and treatment planning. Recently, diffusion models have provided new insights into image segmentation, achieving significant success due to their ability to model nonlinearities. However, existing methods still face challenges, such as false negatives and false positives, caused by image blurring and noise interference, which remain major obstacles.

MACFNet: Detection of Alzheimer's disease via multiscale attention and cross-enhancement fusion network.

Background And Objective: Alzheimer's disease (AD) is a dreaded degenerative disease that results in a profound decline in human cognition and memory. Due to its intricate pathogenesis and the lack of effective therapeutic interventions, early diagnosis plays a paramount role in AD. Recent research based on neuroimaging has shown that the application of deep learning methods by multimodal neural images can effectively detect AD.

CsAGP: Detecting Alzheimer's disease from multimodal images via dual-transformer with cross-attention and graph pooling.

Alzheimer's disease (AD) is a terrible and degenerative disease commonly occurring in the elderly. Early detection can prevent patients from further damage, which is crucial in treating AD. Over the past few decades, it has been demonstrated that neuroimaging can be a critical diagnostic tool for AD, and the feature fusion of different neuroimaging modalities can enhance diagnostic performance.

DSGA-Net: Deeply separable gated transformer and attention strategy for medical image segmentation network.

To address the problems of under-segmentation and over-segmentation of small organs in medical image segmentation. We present a novel medical image segmentation network model with Depth Separable Gating Transformer and a Three-branch Attention module (DSGA-Net). Firstly, the model adds a Depth Separable Gated Visual Transformer (DSG-ViT) module into its Encoder to enhance (i) the contextual links among global, local, and channels and (ii) the sensitivity to location information.

MCSE-U-Net: multi-convolution blocks and squeeze and excitation blocks for vessel segmentation.

Background: Capturing the segmentation of blood vessels by a fundus camera is crucial for the medical evaluation of various retinal vascular issues. However, due to the complicated vascular structure and unclear clinical criteria, the precise segmentation of blood arteries remains very challenging.

Methods: To address this issue, we developed the upgraded multi-convolution block and squeeze and excitation based on the U-shape network (MCSE-U-net) model that segments retinal vessels using a U-shaped network.

GAM-SpCaNet: Gradient awareness minimization-based spinal convolution attention network for brain tumor classification.

Brain tumor is one of the common diseases of the central nervous system, with high morbidity and mortality. Due to the wide range of brain tumor types and pathological types, the same type is divided into different subgrades. The imaging manifestations are complex, making clinical diagnosis and treatment difficult.

CTMLP: Can MLPs replace CNNs or transformers for COVID-19 diagnosis?

Background: Convolutional Neural Networks (CNNs) and the hybrid models of CNNs and Vision Transformers (VITs) are the recent mainstream methods for COVID-19 medical image diagnosis. However, pure CNNs lack global modeling ability, and the hybrid models of CNNs and VITs have problems such as large parameters and computational complexity. These models are difficult to be used effectively for medical diagnosis in just-in-time applications.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) quantification of transient ischemia using a combination method of 5-pool Lorentzian fitting and inverse Z-spectrum analysis.

Background: Chemical exchange saturation transfer (CEST) is a promising method for the detection of biochemical alterations in cancers and neurological diseases. However, the sensitivity of the currently existing quantitative method for detecting ischemia needs further improvement.

Methods: To further improve the quantification of the CEST signal and enhance the CEST detection for ischemia, we used a quantitative analysis method that combines an inverse Z-spectrum analysis and a 5-pool Lorentzian fitting.

Classification of Alzheimer's Disease Based on Weakly Supervised Learning and Attention Mechanism.

The brain lesions images of Alzheimer's disease (AD) patients are slightly different from the Magnetic Resonance Imaging of normal people, and the classification effect of general image recognition technology is not ideal. Alzheimer's datasets are small, making it difficult to train large-scale neural networks. In this paper, we propose a network model (WS-AMN) that fuses weak supervision and an attention mechanism.

NSCGCN: A novel deep GCN model to diagnosis COVID-19.

Aim: Corona Virus Disease 2019 (COVID-19) was a lung disease with high mortality and was highly contagious. Early diagnosis of COVID-19 and distinguishing it from pneumonia was beneficial for subsequent treatment.

Objectives: Recently, Graph Convolutional Network (GCN) has driven a significant contribution to disease diagnosis.

TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model.

Background: As of Feb 27, 2022, coronavirus (COVID-19) has caused 434,888,591 infections and 5,958,849 deaths worldwide, dealing a severe blow to the economies and cultures of most countries around the world. As the virus has mutated, its infectious capacity has further increased. Effective diagnosis of suspected cases is an important tool to stop the spread of the pandemic.

MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images.

Aim: By October 6, 2020, Coronavirus disease 2019 (COVID-19) was diagnosed worldwide, reaching 3,355,7427 people and 1,037,862 deaths. Detection of COVID-19 and pneumonia by the chest X-ray images is of great significance to control the development of the epidemic situation. The current COVID-19 and pneumonia detection system may suffer from two shortcomings: the selection of hyperparameters in the models is not appropriate, and the generalization ability of the model is poor.

Composite Kernel of Mutual Learning on Mid-Level Features for Hyperspectral Image Classification.

By training different models and averaging their predictions, the performance of the machine-learning algorithm can be improved. The performance optimization of multiple models is supposed to generalize further data well. This requires the knowledge transfer of generalization information between models.

Cerebral Micro-Bleeding Detection Based on Densely Connected Neural Network.

Cerebral micro-bleedings (CMBs) are small chronic brain hemorrhages that have many side effects. For example, CMBs can result in long-term disability, neurologic dysfunction, cognitive impairment and side effects from other medications and treatment. Therefore, it is important and essential to detect CMBs timely and in an early stage for prompt treatment.

Alcoholism Identification Based on an AlexNet Transfer Learning Model.

This paper proposes a novel alcoholism identification approach that can assist radiologists in patient diagnosis. AlexNet was used as the basic transfer learning model. The global learning rate was small, at 10, and the iteration epoch number was at 10.

Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling.

Multiple sclerosis is a severe brain and/or spinal cord disease. It may lead to a wide range of symptoms. Hence, the early diagnosis and treatment is quite important.

Multivariate Approach for Alzheimer's Disease Detection Using Stationary Wavelet Entropy and Predator-Prey Particle Swarm Optimization.

Background: The number of patients with Alzheimer's disease is increasing rapidly every year. Scholars often use computer vision and machine learning methods to develop an automatic diagnosis system.

Objective: In this study, we developed a novel machine learning system that can make diagnoses automatically from brain magnetic resonance images.

Improved local ternary patterns for automatic target recognition in infrared imagery.

This paper presents an improved local ternary pattern (LTP) for automatic target recognition (ATR) in infrared imagery. Firstly, a robust LTP (RLTP) scheme is proposed to overcome the limitation of the original LTP for achieving the invariance with respect to the illumination transformation. Then, a soft concave-convex partition (SCCP) is introduced to add some flexibility to the original concave-convex partition (CCP) scheme.