AIMC Topic: Algorithms

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Weakly-supervised semantic segmentation in histology images using contrastive learning and self-training.

Computers in biology and medicine
This paper presents a novel method for weakly-supervised semantic segmentation (WSSS) of histology images, where only global image-level labels are employed. We leverage an existing weakly-supervised object localization (WSOL) method to generate clas...

Meta-analysis of AI-based pulmonary embolism detection: How reliable are deep learning models?

Computers in biology and medicine
RATIONALE AND OBJECTIVES: Deep learning (DL)-based methods show promise in detecting pulmonary embolism (PE) on CT pulmonary angiography (CTPA), potentially improving diagnostic accuracy and workflow efficiency. This meta-analysis aimed to (1) determ...

Transformation trees - Documentation of multimodal image registration.

Computers in biology and medicine
Multimodal image registration plays a key role in creating digital patient models by combining data from different imaging techniques into a single coordinate system. This process often involves multiple sequential and interconnected transformations,...

Myocardial Infarction Detection using Variational Mode Decomposition with Fuzzy Weight Particle Swarm Optimization and Depthwise Separable Convolutional Network.

Computers in biology and medicine
The challenge of precisely recognizing myocardial infarction (MI) from electrocardiographic (ECG) readings stems from the complex nature of these signals.ECG data exhibit both nonlinear and non-stationary properties, making interpretation difficult. ...

Improve robustness to mismatched sampling rate: An alternating deep low-rank approach for exponential function reconstruction and its biomedical magnetic resonance applications.

Journal of magnetic resonance (San Diego, Calif. : 1997)
Undersampling accelerates signal acquisition at the expense of introducing artifacts. Removing these artifacts is a fundamental problem in signal processing and this task is also called signal reconstruction. Through modeling signals as the superimpo...

Deep pixel-wise supervision for skin lesion classification.

Computers in biology and medicine
BACKGROUND: Utilizing automated systems for diagnosing malignant skin lesions promises to improve the early detection of skin diseases and increase patients' survival rates. However, current classification methods primarily focus on global features, ...

Pancreas segmentation in CT scans: A novel MOMUNet based workflow.

Computers in biology and medicine
Automatic pancreas segmentation in CT scans is crucial for various medical applications, including early diagnosis and computer-assisted surgery. However, existing segmentation methods remain suboptimal due to significant pancreas size variations acr...

A Weight-Aware-Based Multisource Unsupervised Domain Adaptation Method for Human Motion Intention Recognition.

IEEE transactions on cybernetics
Accurate recognition of human motion intention (HMI) is beneficial for exoskeleton robots to improve the wearing comfort level and achieve natural human-robot interaction. A classifier trained on labeled source subjects (domains) performs poorly on u...

Improving human brucellosis susceptibility mapping using effective and simultaneously metaheuristic-based feature selection and hyperparameter tuning.

Acta tropica
Human Brucellosis, a neglected zoonotic disease, affects 1.6 to 2.1 million people globally each year. In Iran, it has become a significant health concern, with an average annual incidence rate of 19.91 cases per 100,000 people. This study aims to cr...