AIMC Topic: Algorithms

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Enhanced Regularized Ensemble Encoderdecoder Network for Accurate Brain Tumor Segmentation.

Current medical imaging
BACKGROUND: Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually.

Detection of anemic condition in patients from clinical markers and explainable artificial intelligence.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Anaemia is a commonly known blood illness worldwide. Red blood cell (RBC) count or oxygen carrying capability being insufficient are two ways to describe anaemia. This disorder has an impact on the quality of life. If anaemia is detected ...

A Strategy based on Bioinformatics and Machine Learning Algorithms Reveals Potential Mechanisms of Shelian Capsule against Hepatocellular Carcinoma.

Current pharmaceutical design
BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent and life-threatening form of cancer, with Shelian Capsule (SLC), a traditional Chinese medicine (TCM) formulation, being recommended for clinical treatment. However, the mechanisms underlying ...

Development and validation of a clinical prediction model for glioma grade using machine learning.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive.

Data-driven modelling of brain activity using neural networks, diffusion maps, and the Koopman operator.

Chaos (Woodbury, N.Y.)
We propose a machine-learning approach to construct reduced-order models (ROMs) to predict the long-term out-of-sample dynamics of brain activity (and in general, high-dimensional time series), focusing mainly on task-dependent high-dimensional fMRI ...

Machine Learning to Predict Teratogenicity: Theory and Practice.

Methods in molecular biology (Clifton, N.J.)
Machine learning (ML) is a subfield of artificial intelligence (AI) that consists of developing algorithms that can automatically learn patterns and relationships from data, without being explicitly programmed. It continues to advance with the develo...

Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology.

Radiology
Despite recent advancements in machine learning (ML) applications in health care, there have been few benefits and improvements to clinical medicine in the hospital setting. To facilitate clinical adaptation of methods in ML, this review proposes a s...

Multimodal feature fusion in deep learning for comprehensive dental condition classification.

Journal of X-ray science and technology
BACKGROUND: Dental health issues are on the rise, necessitating prompt and precise diagnosis. Automated dental condition classification can support this need.

A Humane Social Learning-Informed Metaverse: Cultivating Positive Technology Experiences in Digital Learning Environments.

Cyberpsychology, behavior and social networking
Online environments, such as metaverses, provide distinct social environments for people to engage in complex, cognitive, and multidirectional learning and meaning-making experiences. These engaging and influential environments highlight important fa...