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 …
Common and rare fundus diseases identification using vision-language foundation model with knowledge of over 400 diseases
Previous foundation models for retinal images were pre-trained with limited disease
categories and knowledge base. Here we introduce RetiZero, a vision-language foundation …
categories and knowledge base. Here we introduce RetiZero, a vision-language foundation …
Advances in medical image analysis: A comprehensive survey of lung infection detection
S Kordnoori, M Sabeti, H Mostafaei… - IET Image …, 2024 - Wiley Online Library
This research investigates advanced approaches in medical image analysis, specifically
focusing on segmentation and classification techniques, as well as their integration into multi …
focusing on segmentation and classification techniques, as well as their integration into multi …
APPLE: Adversarial Privacy-aware Perturbations on Latent Embedding for Unfairness Mitigation
Ensuring fairness in deep-learning-based segmentors is crucial for health equity. Much effort
has been dedicated to mitigating unfairness in the training datasets or procedures. However …
has been dedicated to mitigating unfairness in the training datasets or procedures. However …
Medical Vision-Language Pretraining through Contrastive Learning of Positive and Negative Mention
WL Wu, J Yang, X Zhu, X Zhang, ZY Liu, M Li, J Wu - openreview.net
In recent years, contrastive learning techniques have achieved significant success and have
been widely applied in both general and medical domains. In the general domain, image …
been widely applied in both general and medical domains. In the general domain, image …