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The paper proposes FreNet, a feature reconfiguration framework with visual priors for medical lesion segmentation, which significantly outperforms state-of-the-art methods on multiple benchmarks by addressing background interference and diverse lesion morphology.
This paper introduces a quality-guided semi-supervised learning framework for medical image segmentation that trains a dedicated predictor to estimate segmentation quality from image-mask pairs, improving pseudolabel reliability and achieving state-of-the-art results across multiple datasets and architectures.
This paper investigates episodic sampling from few-shot learning for class-balanced batch construction in medical image segmentation, showing improved performance under low-data conditions due to reduced overfitting and extended training iterations, with code available on GitHub.
This paper compares cross-validation ensembles to deep ensembles for uncertainty estimation in medical image segmentation. Deep ensembles outperform cross-validation ensembles in calibration and failure detection, while cross-validation ensembles better approximate inter-rater variability.
This paper proposes M2SNet, a multi-scale multi-scale subtraction network for medical image segmentation that uses subtraction operations to capture difference features between encoder levels, improving localization and edge sharpness. It achieves state-of-the-art performance on eleven datasets across four medical imaging modalities.