AIMC Topic: Crowdsourcing

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Aggregating soft labels from crowd annotations improves uncertainty estimation under distribution shift.

PloS one
Selecting an effective training signal for machine learning tasks is difficult: expert annotations are expensive, and crowd-sourced annotations may not be reliable. Recent work has demonstrated that learning from a distribution over labels acquired f...

AI-imputed and crowdsourced price data show strong agreement with traditional price surveys in data-scarce environments.

PloS one
Continuous access to up-to-date food price data is crucial for monitoring food security and responding swiftly to emerging risks. However, in many food-insecure countries, price data is often delayed, lacks spatial detail, or is unavailable during cr...

Impact of Image Content on Medical Crowdfunding Success: A Machine Learning Approach.

Journal of medical Internet research
BACKGROUND: As crowdfunding sites proliferate, visual content often serves as the initial bridge connecting a project to its potential backers, underscoring the importance of image selection in effectively engaging an audience.

Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative.

EBioMedicine
BACKGROUND: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term...

Machine learning surveillance of foodborne infectious diseases using wastewater microbiome, crowdsourced, and environmental data.

Water research
Clostridium perfringens (CP) is a common cause of foodborne infection, leading to significant human health risks and a high economic burden. Thus, effective CP disease surveillance is essential for preventive and therapeutic interventions; however, c...

Boosting wisdom of the crowd for medical image annotation using training performance and task features.

Cognitive research: principles and implications
A crucial bottleneck in medical artificial intelligence (AI) is high-quality labeled medical datasets. In this paper, we test a large variety of wisdom of the crowd algorithms to label medical images that were initially classified by individuals recr...

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge.

Scientific reports
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-de...

Crowdsourcing image segmentation for deep learning: integrated platform for citizen science, paid microtask, and gamification.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: Segmentation is crucial in medical imaging. Deep learning based on convolutional neural networks showed promising results. However, the absence of large-scale datasets and a high degree of inter- and intra-observer variations pose a bottl...

Crowd-Sourced Deep Learning for Intracranial Hemorrhage Identification: Wisdom of Crowds or Laissez-Faire.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Researchers and clinical radiology practices are increasingly faced with the task of selecting the most accurate artificial intelligence tools from an ever-expanding range. In this study, we sought to test the utility of ensem...

The Natural Robotics Contest: crowdsourced biomimetic design.

Bioinspiration & biomimetics
Biomimetic and bioinspired design is not only a potent resource for roboticists looking to develop robust engineering systems or understand the natural world. It is also a uniquely accessible entry point into science and technology. Every person on E...