AIMC Topic: Datasets as Topic

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An open dataset and machine learning algorithms for Niacin Skin-Flushing Response based screening of psychiatric disorders.

BMC psychiatry
BACKGROUND: Niacin Skin-Flushing Response (NSR) has emerged as a promising objective biomarker for the precise diagnosis of mental disorders. However, its diagnostic potential has been constrained by the limitations of traditional statistical approac...

Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines.

BMC medical imaging
BACKGROUND: As an important branch of machine learning pipelines in medical imaging, radiomics faces two major challenges namely reproducibility and accessibility. In this work, we introduce open-radiomics, a set of radiomics datasets along with a co...

Open-access ultrasonic diaphragm dataset and an automatic diaphragm measurement using deep learning network.

Respiratory research
BACKGROUND: The assessment of diaphragm function is crucial for effective clinical management and the prevention of complications associated with diaphragmatic dysfunction. However, current measurement methodologies rely on manual techniques that are...

MIDAS: a technology-enabled hub-and-spoke system for the collection and dissemination of high-quality medical datasets in India.

BMC medical informatics and decision making
BACKGROUND: The need for better AI models fuels the demand for larger and larger high-quality datasets with significant diversity. Over the years, many medical imaging datasets have been published globally, but existing datasets do not contain enough...

Learning from small datasets-review of workshop 6 of the 10th International BCI Meeting 2023.

Journal of neural engineering
In a brain-computer interface (BCI), a primary objective is to reduce calibration time by recording as few as possible novel data points to (re-)train decoder models.Minimizing the calibration can be crucial for enhancing the usability of a BCI appli...

Tailoring task arithmetic to address bias in models trained on multi-institutional datasets.

Journal of biomedical informatics
OBJECTIVE: Multi-institutional datasets are widely used for machine learning from clinical data, to increase dataset size and improve generalization. However, deep learning models in particular may learn to recognize the source of a data element, lea...

Out-of-distribution reject option method for dataset shift problem in early disease onset prediction.

Scientific reports
Machine learning is increasingly used to predict lifestyle-related disease onset using health and medical data. However, its predictive accuracy for use is often hindered by dataset shift, which refers to discrepancies in data distribution between th...

A digital photography dataset for Vaccinia Virus plaque quantification using Deep Learning.

Scientific data
Virological plaque assay is the major method of detecting and quantifying infectious viruses in research and diagnostic samples. Furthermore, viral plaque phenotypes contain information about the life cycle and spreading mechanism of the virus formin...

Medical image translation with deep learning: Advances, datasets and perspectives.

Medical image analysis
Traditional medical image generation often lacks patient-specific clinical information, limiting its clinical utility despite enhancing downstream task performance. In contrast, medical image translation precisely converts images from one modality to...

Diabetic retinopathy detection based on mobile maxout network and weber local descriptor feature selection using retinal fundus image.

Scientific reports
Retinal screening provides for earlier detection of diabetic retinopathy (DR) as well as prompt diagnosis. Recognizing DR utilizing color fundus imaging needs qualified specialists to know about the presence and significance of a few insignificant fe...