Medical Segmentation Decathlon
PulseAugur coverage of Medical Segmentation Decathlon — every cluster mentioning Medical Segmentation Decathlon across labs, papers, and developer communities, ranked by signal.
-
Active Learning enhances Differentiable NAS for 3D medical image segmentation
Researchers have developed Active-DiNTS, a novel approach that integrates Active Learning with differentiable Neural Architecture Search (NAS) for 3D medical image segmentation. This method jointly optimizes network top…
-
New MRFFU-Net architecture enhances MRI segmentation accuracy
Researchers have developed a new deep learning architecture called Multi-Resolution Feature Fusion U-Net (MRFFU-Net) designed to improve the segmentation of magnetic resonance imaging (MRI) scans. This novel architectur…
-
New AI models offer improved brain tumor segmentation with efficiency gains
Researchers have developed DALight-3D, a more computationally efficient 3D U-Net variant for segmenting brain tumors from multi-modal MRI scans. This model achieves a favorable accuracy-efficiency trade-off, outperformi…
-
SAM model shows stable spleen segmentation in CT scans despite domain shifts
Researchers evaluated the robustness of the Segment Anything Model (SAM) for spleen segmentation in abdominal CT scans, simulating various domain shifts like noise and resolution changes. The study found that SAM mainta…