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Found 6 papers, 1 papers with code
Augmentation based unsupervised domain adaptation
The insertion of deep learning in medical image analysis had lead to the development of state-of-the art strategies in several applications such a disease classification, as well as abnormality detection and segmentation.
DermX: an end-to-end framework for explainable automated dermatological diagnosis
We assess the explanation performance in terms of identification and localization by comparing model-selected with dermatologist-selected explanations, and gradient-weighted class-activation maps with dermatologist explanation maps, respectively.
Test-time Unsupervised Domain Adaptation
Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain).
Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning
Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to $n$ target domains (as long as there is paired data covering all domains).
Knowledge distillation for semi-supervised domain adaptation
As a result, their performance is significantly lower on data from unseen sources compared to the performance on data from the same source as the training data.
Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid attenuated inversion recovery (FLAIR) sequence --where lesions appear hyperintense--.
