AIMC Topic: Crowdsourcing

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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...

Does Surgeon Experience Correlate with Crowd-Sourced Skill Assessment in Robotic Bariatric Surgery?

The American surgeon
BACKGROUND: The Global Evaluative Assessment of Robotic Skills (GEARS) rubric provides a measure of skill in robotic surgery. We hypothesize surgery performed by more experienced operators will be associated with higher GEARS scores.

Designing all-pay auctions using deep learning and multi-agent simulation.

Scientific reports
We propose a multi-agent learning approach for designing crowdsourcing contests and All-Pay auctions. Prizes in contests incentivise contestants to expend effort on their entries, with different prize allocations resulting in different incentives and...

Can images crowdsourced from the internet be used to train generalizable joint dislocation deep learning algorithms?

Skeletal radiology
OBJECTIVE: Deep learning has the potential to automatically triage orthopedic emergencies, such as joint dislocations. However, due to the rarity of these injuries, collecting large numbers of images to train algorithms may be infeasible for many cen...

An Exploratory Approach to Deriving Nutrition Information of Restaurant Food from Crowdsourced Food Images: Case of Hartford.

Nutrients
Deep learning models can recognize the food item in an image and derive their nutrition information, including calories, macronutrients (carbohydrates, fats, and proteins), and micronutrients (vitamins and minerals). This technology has yet to be imp...

Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO).

PLoS computational biology
Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying ...