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Found 3 papers, 1 papers with code
CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation
Direct automatic segmentation of objects from 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying a number of individual objects with complex geometries within a large volume under investigation.
Manipulating Medical Image Translation with Manifold Disentanglement
In this work, we propose Manifold Disentanglement Generative Adversarial Network (MDGAN), a novel image translation framework that explicitly models these two types of features.
Generalisable 3D Fabric Architecture for Streamlined Universal Multi-Dataset Medical Image Segmentation
However, medical image datasets have diverse-sized images and features, and developing a model simultaneously for multiple datasets is challenging.
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