AIMC Topic: Machine Learning

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CYCLONE: recycle contrastive learning for integrating single-cell gene expression data.

BMC bioinformatics
BACKGROUND: Combining single-cell transcriptome sequencing results from several batches reduces batch effect, which improves our understanding of cellular identity and function.

Contrastive learning-driven framework for neuron morphology classification.

Scientific reports
The Neuron morphology classification is a critical task in neuroscience research, as the morphological features of neurons are closely linked to the functional characteristics of neural circuits. However, traditional classification methods often stru...

A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference.

Scientific reports
Medical image analysis using deep learning algorithms has become a basis of modern healthcare, enabling early detection, diagnosis, treatment planning, and disease monitoring. However, sharing sensitive raw medical data with third parties for analysi...

AMuCS: Affective multimodal Counter-Strike video game dataset.

Scientific data
Video games are a versatile and multi-faceted stimulus which can elicit complex player experiences. As a consequence, several datasets have been curated or created for studying human cognition, behaviours, and physiological responses where video game...

Machine Learning-Based Cognitive Assessment With The Autonomous Cognitive Examination: Randomized Controlled Trial.

Journal of medical Internet research
BACKGROUND: The rising prevalence of dementia necessitates a scalable solution to cognitive assessments. The Autonomous Cognitive Examination (ACoE) is a foundational cognitive test for the phenotyping of cognitive symptoms across the primary cogniti...

The grand challenges of learning medical robot autonomy.

Science robotics
Most medical robots depend on human operators for sensing, decision-making, and action during procedures. Future progress depends on enabling robots to take on these capabilities. Although learning-based approaches provide remarkable promise toward a...

AI in therapeutic and assistive exoskeletons and exosuits: Influences on performance and autonomy.

Science robotics
Therapeutic and assistive exoskeletons and exosuits show promise in both clinical and real-world settings. Improving their autonomy can enhance usability, effectiveness, and cost efficiency. This Review presents a generic control framework for autono...

AI search, physician removal: Bronchoscopy robot bridges collaboration in foreign body aspiration.

Science robotics
Bronchial foreign body aspiration is a life-threatening condition with a high incidence across diverse populations, requiring urgent diagnosis and treatment. However, the limited availability of skilled practitioners and advanced medical equipment in...

Identification of DNA damage response and crotonylation-related biomarkers for lung adenocarcinoma via machine learning and WGCNA.

Clinical and experimental medicine
DNA damage response (DDR) and crotonylation occur frequently in lung adenocarcinoma (LUAD), but their relationship is yet to be elucidated. RNA sequencing data from LUAD patients in GSE27262 and GSE140797 datasets were obtained. DDR-crotonylation-rel...