Titlebook: Domain Adaptation and Representation Transfer; 5th MICCAI Workshop, Lisa Koch,M. Jorge Cardoso,Dong Yang Conference proceedings 2024 The Ed

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,DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy Classificati the performance of models trained with the independent and identically distributed (i.i.d) assumption deteriorates when deployed in the real world. This problem is exacerbated in the medical imaging context due to variations in data acquisition across clinical centers, medical apparatus, and patien

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,SEDA: Self-ensembling ViT with Defensive Distillation and Adversarial Training for Robust Chest X-Recent Deep Learning solutions, which can hinder future adoption. Particularly, the vulnerability of Vision Transformer (ViT) to adversarial, privacy, and confidentiality attacks raise serious concerns about their reliability in medical settings. This work aims to enhance the robustness of self-ensem

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A Continual Learning Approach for Cross-Domain White Blood Cell Classification,al settings, data sources, and disease classifications, it is necessary to update machine learning classification models regularly for practical real-world use. Such models significantly benefit from sequentially learning from incoming data streams without forgetting previously acquired knowledge. H

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Metadata Improves Segmentation Through Multitasking Elicitation,had limited use in deep learning methods, for semantic segmentation in particular. Here, we incorporate metadata by employing a channel modulation mechanism in convolutional networks and study its effect on semantic segmentation tasks. We demonstrate that metadata as additional input to a convolutio

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https://doi.org/10.1007/978-3-031-13173-8s. Instead of regarding the entire dataset as a source or target domain, the dataset is processed based on the dominant factor of data variations, which is the scanner manufacturer. Afterwards, the target domain’s feature space is aligned pairwise with respect to each source domain’s feature map. Ex

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https://doi.org/10.1007/978-3-031-13913-0obust features, we can achieve better segmentation and detection results. Additionally, MultiVT improves generalization capabilities without applying domain adaptive techniques - a characteristic which renders our method suitable for use in real-world applications.

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Svetlana G. Cheglakova,Тatyana А. Zhuravleva on datasets containing different types of source-target domain combinations to demonstrate the versatility and robustness of our method. We confirm that our method outperforms the state-of-the-art on all datasets.

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