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näkemys - Domain Generalization in Medical Imaging
Domain Generalization in Deep Learning for Endoscopic Imaging: A Novel Approach to Handle Out-of-Distribution Modality Shifts
This research paper introduces a novel deep learning framework that enhances the generalizability of endoscopic image segmentation models, enabling them to effectively handle out-of-distribution data from different imaging modalities.
RetiGen: A Framework for Generalized Retinal Diagnosis Using Multi-View Fundus Images
提案されたRetiGenフレームワークは、多視点眼底画像を使用して、医療画像のドメイン汎化を向上させる革新的な方法を紹介しています。
ConDiSR: Contrastive Disentanglement and Style Regularization for Medical Image Classification
Novel method ConDiSR improves medical image classification through contrastive disentanglement and style regularization.
Learning to Generalize towards Unseen Domains via Style Invariant Model for Disease Detection from Chest X-rays
The author presents a novel approach using style randomization modules to improve cross-domain performance in disease detection from chest X-rays, achieving significant results compared to existing methods.
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