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Einblick - Medical Imaging AI
Virtual Imaging Trials Reveal Biases and Improve Reliability of AI Systems for COVID-19 Diagnosis
Virtual imaging trials can identify biases and improve the reliability of AI models for COVID-19 diagnosis by providing controlled, independent testing data and insights into the impact of patient and imaging factors on model performance.
COMPRER: A Multimodal Multi-Objective Pretraining Framework for Enhanced Medical Image Representation
COMPRERは、医用画像表現を向上させるための新しいマルチモーダル、マルチオブジェクティブ事前トレーニングフレームワークを提供します。
Universal Debiased Editing for Fair Medical Image Classification Study
The study introduces Universal Debiased Editing (UDE) to address biases in medical image classification by masking spurious correlations. The approach aims to promote fairness and utility across various patient groups and diseases.
Domain Adaptation, Explainability & Fairness in AI for Medical Image Analysis: COVID-19 Diagnosis Based on 3-D Chest CT-Scans
The author discusses the importance of domain adaptation, explainability, and fairness in AI for medical image analysis, focusing on the diagnosis of COVID-19 using 3-D chest CT scans.
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