Scribble-guided dose prediction uses a completion module with supervoxel regularization to generate dense masks from sparse labels and conditions a dose network on them, achieving strong performance on the GDP-HMM dataset with lower annotation cost.
IEEE Transactions on Neural Networks and Learning Systems (2025)
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CA-GCL combines global contrastive learning with permutation-invariant text augmentation to deliver zero-shot 3D medical abnormality detection that is more robust to prompt changes than prior FVLP methods.
citing papers explorer
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ScribbleDose: Scribble-Guided Dose Prediction in Radiotherapy
Scribble-guided dose prediction uses a completion module with supervoxel regularization to generate dense masks from sparse labels and conditions a dose network on them, achieving strong performance on the GDP-HMM dataset with lower annotation cost.
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CA-GCL: Cross-Anatomy Global-Local Contrastive Learning for Robust 3D Medical Image Understanding
CA-GCL combines global contrastive learning with permutation-invariant text augmentation to deliver zero-shot 3D medical abnormality detection that is more robust to prompt changes than prior FVLP methods.