Learning Unbiased Image Segmentation: A Case Study with Plain Knee
  Radiographs
Nickolas Littlefield Johannes F. Plate Kurt R. Weiss Ines Lohse Avani Chhabra Ismaeel A. Siddiqui Zoe Menezes George Mastorakos Sakshi Mehul Thakar Mehrnaz Abedian Matthew F. Gong Luke A. Carlson Hamidreza Moradi Soheyla Amirian Ahmad P. Tafti
Automatic segmentation of knee bony anatomy is essential in orthopedics, and
it has been around for several years in both pre-operative and post-operative
settings. While deep learning algorithms have demonstrated exceptional
performance in medical image analysis, the assessment of fairness and potential
biases within these models remains limited. This study aims to revisit deep
learning-powered knee-bony anatomy segmentation using plain radiographs to
uncover visible gender and racial biases. The current contribution offers the
potential to advance our understanding of biases, and it provides practical
insights for researchers and practitioners in medical imaging. The proposed
mitigation strategies mitigate gender and racial biases, ensuring fair and
unbiased segmentation results. Furthermore, this work promotes equal access to
accurate diagnoses and treatment outcomes for diverse patient populations,
fostering equitable and inclusive healthcare provision.