Addressing fairness issues in deep learning-based medical image analysis: a systematic review
Deep learning algorithms have demonstrated remarkable efficacy in various medical image
analysis (MedIA) applications. However, recent research highlights a performance disparity …
analysis (MedIA) applications. However, recent research highlights a performance disparity …
Challenges and Potential of Artificial Intelligence in Neuroradiology
Purpose Artificial intelligence (AI) has emerged as a transformative force in medical
research and is garnering increased attention in the public consciousness. This represents a …
research and is garnering increased attention in the public consciousness. This represents a …
Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging
Objective Artificial intelligence (AI) models trained using medical images for clinical tasks
often exhibit bias in the form of subgroup performance disparities. However, since not all …
often exhibit bias in the form of subgroup performance disparities. However, since not all …
Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population
S Yao, F Dai, P Sun, W Zhang, B Qian, H Lu - Nature Communications, 2024 - nature.com
Artificial Intelligence (AI) models for medical diagnosis often face challenges of
generalizability and fairness. We highlighted the algorithmic unfairness in a large thyroid …
generalizability and fairness. We highlighted the algorithmic unfairness in a large thyroid …
Where, why, and how is bias learned in medical image analysis models? A study of bias encoding within convolutional networks using synthetic data
Background Understanding the mechanisms of algorithmic bias is highly challenging due to
the complexity and uncertainty of how various unknown sources of bias impact deep …
the complexity and uncertainty of how various unknown sources of bias impact deep …
How Fair are Medical Imaging Foundation Models?
While medical imaging foundation models have led to significant improvements across
various tasks, the pivotal issue of subgroup fairness in these foundation models has …
various tasks, the pivotal issue of subgroup fairness in these foundation models has …
(Predictable) performance bias in unsupervised anomaly detection
Background With the ever-increasing amount of medical imaging data, the demand for
algorithms to assist clinicians has amplified. Unsupervised anomaly detection (UAD) models …
algorithms to assist clinicians has amplified. Unsupervised anomaly detection (UAD) models …
A Flexible Framework for Simulating and Evaluating Biases in Deep Learning-Based Medical Image Analysis
Despite the remarkable advances in deep learning for medical image analysis, it has
become evident that biases in datasets used for training such models pose considerable …
become evident that biases in datasets used for training such models pose considerable …
Identifying biases in a multicenter MRI database for Parkinson's disease classification: Is the disease classifier a secret site classifier?
Sharing multicenter imaging datasets can be advantageous to increase data diversity and
size but may lead to spurious correlations between site-related biological and non-biological …
size but may lead to spurious correlations between site-related biological and non-biological …
SMOTE-MRS: A Novel SMOTE-Multiresolution Sampling technique for imbalanced distribution to improve prediction of anemia
Anemia is a widespread worldwide health problem that has a substantial effect on groups
who are particularly susceptible. The objective of this work is to improve the diagnosis of …
who are particularly susceptible. The objective of this work is to improve the diagnosis of …