Enhancing Dataset Quality for AI in Radiology: Challenges and Solutions-Reference-Cited by-同舟云学术

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Enhancing Dataset Quality for AI in Radiology: Challenges and Solutions

Published:2024-09-02 Issue: Volume: Page:
ISSN:2581-9178
Container-title:Journal of Gastrointestinal and Abdominal Radiology
language:en
Short-container-title:Journal of Gastrointestinal and Abdominal Radiology

Author:

Rengan Vinayak¹,Lalwani Devansh²,Bhat Swapnil³,Sundaram Pravin Meenashi⁴^ORCID

Affiliation:

1. Department of Pediatric Surgery, SMS Medical College, Jaipur, Rajasthan, India

2. Seth GS Medical College & KEM Hospital, Mumbai, Maharashtra, India

3. AI Researcher, Miko.ai, Mumbai, Maharashtra, India

4. Department of Renal Transplant Surgery, Sheffield Kidney Institute, Sheffield Teaching Hospitals, Sheffield, United Kingdom

Publisher

Georg Thieme Verlag KG

Link

http://www.thieme-connect.de/products/ejournals/pdf/10.1055/s-0044-1790232.pdf

Reference11 articles.

1. Understanding biases and disparities in radiology AI datasets: a review;S Tripathi;J Am Coll Radiol,2023

2. Exploring large-scale public medical image datasets;L Oakden-Rayner;Acad Radiol,2020

3. Preparing medical imaging data for machine learning;M J Willemink;Radiology,2020

4. Biases associated with database structure for COVID-19 detection in X-ray images;D Arias-Garzón;Sci Rep,2023

5. Overcoming the challenges in the development and implementation of artificial intelligence in radiology: a comprehensive review of solutions beyond supervised learning;G S Hong;Korean J Radiol,2023

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