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Found 3 papers, 1 papers with code
PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix Augmentation
In weakly supervised medical image segmentation, the absence of structural priors and the discreteness of class feature distribution present a challenge, i. e., how to accurately propagate supervision signals from local to global regions without excessively spreading them to other irrelevant regions?
LanDA: Language-Guided Multi-Source Domain Adaptation
Multi-Source Domain Adaptation (MSDA) aims to mitigate changes in data distribution when transferring knowledge from multiple labeled source domains to an unlabeled target domain.
Auto Machine Learning for Medical Image Analysis by Unifying the Search on Data Augmentation and Neural Architecture
To address the problem, an improved augmentation search strategy, named Augmented Density Matching, was proposed by randomly sampling policies from a prior distribution for training.
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