Research map / Adversarial robustness and security / Attacks on text and graphs
Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the complete NLCE module, combining non-local dependencies and global-context encoding (ablation) | NLCEN · JSRT lung segmentation under adversarial attacks | robustness | Helped |
| Add non-local dependency modeling (ablation) | NLCEN · JSRT lung segmentation under adversarial attacks | robustness | Helped |
| Add global-context encoding (ablation) | NLCEN · JSRT lung segmentation under adversarial attacks | robustness | Helped |
| Fine-tune NLCEN without freezing any layer (ablation) | NLCEN · JSRT lung segmentation under adversarial attacks | accuracy and robustness | Helped |
| Add NLCE modules | SLSDeep, NWCN, UNet, InvertNet, and CDNN · JSRT lung segmentation under adversarial attacks | DIC and JSC | Helped |
| Use NLCEN instead of the compared state-of-the-art methods | JSRT lung segmentation · Clean images and targeted Iterative FGSM ε=32 | segmentation accuracy | Mixed |
| Use NLCEN instead of the compared state-of-the-art methods | ISBI 2016 skin lesion segmentation · Clean images and targeted Iterative FGSM ε=32 | segmentation accuracy | Helped |
Closest papers by meaning
- nnWNet: Rethinking the Use of Transformers in Biomedical Image Segmentation and Calling for a Unified Evaluation Benchmark (CVPR 2025 · similarity 0.87)
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- Attacking CNNs in Histopathology with SNAP: Sporadic and Naturalistic Adversarial Patches (Student Abstract) (AAAI 2024 · similarity 0.84)
- Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention (CVPR 2025 · similarity 0.84)
- Show and Segment: Universal Medical Image Segmentation via In-Context Learning (CVPR 2025 · similarity 0.83)
- Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It (NeurIPS 2026 · similarity 0.83)
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