A new quality-guided approach for semi-supervised medical image segmentation that trains a predictor on synthetic errors to enhance pseudolabel handling.
In: European Conference on Computer Vision
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
MS-DKC is a dataset knowledge card framework that maps image, morphology, supervision, context, and risk descriptors to design priors and failure modes, shown to produce dataset-specific model adaptations with improved metrics on DRIVE, ISIC2018, and ACDC.
ResNet-34 encoder with MLP-based lightweight decoder reports 90.33% mean DSC and 97.37% accuracy on FeTA 2021 fetal brain MRI segmentation, outperforming UNet variants.
citing papers explorer
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Quality-Guided Semi-Supervised Learning for Medical Image Segmentation
A new quality-guided approach for semi-supervised medical image segmentation that trains a predictor on synthetic errors to enhance pseudolabel handling.
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MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models
MS-DKC is a dataset knowledge card framework that maps image, morphology, supervision, context, and risk descriptors to design priors and failure modes, shown to produce dataset-specific model adaptations with improved metrics on DRIVE, ISIC2018, and ACDC.
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ResNet-34 with Lightweight Decoder for Accurate and Efficient Segmentation of Fetal Brain MRI
ResNet-34 encoder with MLP-based lightweight decoder reports 90.33% mean DSC and 97.37% accuracy on FeTA 2021 fetal brain MRI segmentation, outperforming UNet variants.