AIMC Topic: Algorithms

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Differentiable self-supervised clustering with intrinsic interpretability.

Neural networks : the official journal of the International Neural Network Society
Self-supervised clustering has garnered widespread attention due to its ability to discover latent clustering structures without the need for external labels. However, most existing approaches on self-supervised clustering lack of inherent interpreta...

Artificial intelligence and machine learning for anaphylaxis algorithms.

Current opinion in allergy and clinical immunology
PURPOSE OF REVIEW: Anaphylaxis is a severe, potentially life-threatening allergic reaction that requires rapid identification and intervention. Current management includes early recognition, prompt administration of epinephrine, and immediate medical...

Input-to-state stability of delayed memristor-based inertial neural networks via non-reduced order method.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with the input-to-state stability (ISS) for a kind of delayed memristor-based inertial neural networks (DMINNs). Based on the nonsmooth analysis and stability theory, novel delay-dependent and delay-independent criteria on the...

Pitfalls in Interpretive Applications of Artificial Intelligence in Radiology.

AJR. American journal of roentgenology
Interpretive artificial intelligence (AI) tools are poised to change the future of radiology. However, certain pitfalls may pose particular challenges for optimal AI interpretative performance. These include anatomic variants, age-related changes, po...

Continual medical image denoising based on triplet neural networks collaboration.

Computers in biology and medicine
BACKGROUND: When multiple tasks are learned consecutively, the old model parameters may be overwritten by the new data, resulting in the phenomenon that the new task is learned and the old task is forgotten, which leads to catastrophic forgetting. Mo...

Cytopathic Effect Detection and Clonal Selection using Deep Learning.

Pharmaceutical research
PURPOSE: In biotechnology, microscopic cell imaging is often used to identify and analyze cell morphology and cell state for a variety of applications. For example, microscopy can be used to detect the presence of cytopathic effects (CPE) in cell cul...

A Deep-Learning-Based CPR Action Standardization Method.

Sensors (Basel, Switzerland)
In emergency situations, ensuring standardized cardiopulmonary resuscitation (CPR) actions is crucial. However, current automated external defibrillators (AEDs) lack methods to determine whether CPR actions are performed correctly, leading to inconsi...

Compressed Deep Learning Models for Wearable Atrial Fibrillation Detection through Attention.

Sensors (Basel, Switzerland)
Deep learning (DL) models have shown promise for the accurate detection of atrial fibrillation (AF) from electrocardiogram/photoplethysmography (ECG/PPG) data, yet deploying these on resource-constrained wearable devices remains challenging. This stu...

Adaptative machine vision with microsecond-level accurate perception beyond human retina.

Nature communications
Visual adaptive devices have potential to simplify circuits and algorithms in machine vision systems to adapt and perceive images with varying brightness levels, which is however limited by sluggish adaptation process. Here, the avalanche tuning as f...

Low-contrast lesion detection in neck CT: a multireader study comparing deep learning, iterative, and filtered back projection reconstructions using realistic phantoms.

European radiology experimental
BACKGROUND: Computed tomography (CT) reconstruction algorithms can improve image quality, especially deep learning reconstruction (DLR). We compared DLR, iterative reconstruction (IR), and filtered back projection (FBP) for lesion detection in neck C...