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Found 2 papers, 1 papers with code
A Classifier-Free Incremental Learning Framework for Scalable Medical Image Segmentation
We demonstrate the flexibility of our method in handling varying class numbers within a unified network and its capacity for incremental learning.
Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation
A versatile medical image segmentation model applicable to images acquired with diverse equipment and protocols can facilitate model deployment and maintenance.
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