Bias in Tomography Artificial Intelligence

A special issue of Tomography (ISSN 2379-139X). This special issue belongs to the section "Artificial Intelligence in Medical Imaging".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 258

Special Issue Editor


E-Mail Website
Guest Editor
1. Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada
2. Department of Statistical Sciences, University of Toronto, Toronto, ON M5T 1W7, Canada
3. Institute of Medical Science, University of Toronto, Toronto, ON M5T 1W7, Canada
Interests: artificial Intelligence; biostatistics; machine learning; medical images

Special Issue Information

Dear Colleagues,

The development and deployment of machine learning algorithms for medical imaging has become increasingly popular in recent years. These algorithms rely on large amounts of training data to learn patterns and features in the images that can be used for tasks such as segmentation and classification. However, machine learning algorithms can suffer from various forms of bias, such as sampling bias, measurement bias, and algorithmic bias. Sampling bias can occur when the training data do not accurately represent the population that the algorithm will be used on. Measurement bias can occur when the measurements used in the training data are not consistent or standardized. Algorithmic bias can occur when the algorithm is designed or trained in a way that reflects societal biases. Addressing bias in machine learning algorithms for medical imaging is critical for ensuring accurate and equitable healthcare.

This Special Issue on bias in tomography artificial intelligence provides an opportunity for researchers to explore and propose solutions for mitigating bias in machine learning algorithms for medical imaging. Submissions can include studies that investigate the impact of bias in machine learning algorithms for medical imaging, as well as proposals for improving data collection, pre-processing, and algorithmic design to mitigate bias. We welcome manuscripts that explore new and innovative approaches to improving the fairness and accuracy of machine learning algorithms for tomography medical imaging.

Dr. Pascal N. Tyrrell
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Tomography is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • medical imaging
  • artificial intelligence
  • machine learning
  • bias
  • fairness diagnosis

Published Papers

This special issue is now open for submission.
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