Research map / Detection and segmentation / CNN features
HyperSegNAS: Bridging One-Shot Neural Architecture Search With 3D Medical Image Segmentation Using HyperNet
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add a topology- and image-conditioned Meta-Assistant Network during super-net training (ablation) | HyperSegNAS · Pancreas (25 sampled architectures) | DSC Avg.: 52.92 -> 60.13 | Helped |
| Add architecture-vector conditioning alongside image-vector conditioning (ablation) | HyperSegNAS Meta-Assistant Network · Pancreas (25 sampled architectures) | DSC Avg.: 56.53 -> 60.13 | Helped |
| Anneal the Meta-Assistant Network away before evaluation (ablation) | HyperSegNAS · Pancreas (25 sampled architectures) | DSC Avg.: 30.16 -> 59.15 | Helped |
| Add image-vector conditioning to the Meta-Assistant Network (ablation) | HyperSegNAS · Pancreas (25 sampled architectures) | DSC Avg.: 52.92 -> 56.53 | Helped |
| Use HyperSegNAS rather than DiNTS for architecture search | 3D segmentation architectures · Pancreas and Tumor (MSD) | DSC Avg.: 68.19 -> 68.58 | Helped |
| Use HyperSegNAS rather than DiNTS for architecture search | 3D segmentation architectures · Lung Tumor (MSD) | DSC1: 74.75 -> 76.72 | Helped |
| Use HyperSegNAS rather than nnUNet | 3D segmentation architectures · Hepatic Vessel and Tumor (MSD) | DSC Avg.: 69.12 -> 68.23 | Hurt |
| Use HyperSegNAS rather than DiNTS | 3D segmentation architectures · Brain Tumor (MSD) | NSD Avg.: 84.73 -> 84.69 | Hurt |
| Use HyperSegNAS rather than nnUNet | 3D segmentation architectures · Hippocampus (MSD) | DSC Avg.: 89.46 -> 89.34 | Hurt |
| Use HyperSegNAS rather than the strongest listed baseline | 3D segmentation architectures · Hippocampus (MSD) | NSD Avg.: 97.75 -> 97.91 | Helped |
Closest papers by meaning
- Upping the Game: How 2D U-Net Skip Connections Flip 3D Segmentation (NeurIPS 2024 · similarity 0.86)
- EffiDec3D: An Optimized Decoder for High-Performance and Efficient 3D Medical Image Segmentation (CVPR 2025 · similarity 0.85)
- UniPro: Unified Multi-Mode Medical Image Segmentation from 2D Images to 3D Volumes via Propagation (NeurIPS 2026 · similarity 0.85)
- nnWNet: Rethinking the Use of Transformers in Biomedical Image Segmentation and Calling for a Unified Evaluation Benchmark (CVPR 2025 · similarity 0.85)
- Revisiting MAE Pre-training for 3D Medical Image Segmentation (CVPR 2025 · similarity 0.85)
- MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling (CVPR 2024 · similarity 0.85)
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