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README.md

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+---
+license: mit
+tags:
+  - medical-imaging
+  - self-supervised-learning
+  - masked-autoencoder
+  - 3d-ct
+  - pretraining
+---
+
+# NEMESIS
+
+**Superpatch-based 3D Medical Image Self-Supervised Pretraining via Noise-Enhanced Dual-Masking**
+
+> IEEE AICAS 2026
+
+## Overview
+
+NEMESIS is a self-supervised pretraining framework for 3D CT volumes using:
+- **Superpatch processing** (128³ sub-volumes) — memory-efficient ViT pretraining
+- **Dual-masking (MATB)** — plane-wise (xy) + axis-wise (z) masking, exploiting CT anisotropy
+- **NEMESIS Tokens (NTs)** — learnable tokens summarising visible patches via cross-attention
+- **Noise-enhanced reconstruction** — Gaussian noise injection for regularisation
+
+### Key result (BTCV organ classification, frozen linear probe)
+
+| Method | AUROC |
+|---|---|
+| **NEMESIS (frozen)** | **0.9633** |
+| SuPreM (fine-tuned) | 0.9493 |
+| VoCo (fine-tuned) | 0.9387 |
+
+## Checkpoints
+
+| File | embed_dim | depth | mask_ratio |
+|---|---|---|---|
+| `MAE_768_0.5.pt` | 768 | 6 | 0.5 | ← **main model (paper)** |
+| `MAE_768_0.25.pt` | 768 | 6 | 0.25 | ablation |
+| `MAE_768_0.75.pt` | 768 | 6 | 0.75 | ablation |
+| `MAE_576_0.5.pt` | 576 | 6 | 0.5 | ablation |
+| `MAE_384_0.5.pt` | 384 | 6 | 0.5 | ablation |
+| (others) | | | | embed_dim × mask_ratio ablations |
+
+## Usage
+
+```bash
+pip install huggingface_hub
+huggingface-cli download whilethis/NEMESIS MAE_768_0.5.pt --local-dir pretrained/
+```
+
+```python
+import torch
+from nemesis.models.mae import MAEgic3DMAE
+
+ckpt = torch.load("pretrained/MAE_768_0.5.pt", map_location="cpu")
+model = MAEgic3DMAE(
+    embed_dim=768, depth=6, num_heads=8,
+    decoder_embed_dim=128, decoder_depth=3,
+    num_maegic_tokens=8,
+)
+model.load_state_dict(ckpt["model_state_dict"])
+encoder = model.encoder
+```
+
+## Code
+
+[https://github.com/whilethis00/NEMESIS-public](https://github.com/whilethis00/NEMESIS-public)
+
+## Citation
+
+```bibtex
+@inproceedings{jung2026nemesis,
+  title     = {{NEMESIS}: Superpatch-based 3{D} Medical Image Self-Supervised Pretraining via Noise-Enhanced Dual-Masking},
+  author    = {Jung, Hyeonseok and others},
+  booktitle = {IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)},
+  year      = {2026},
+}
+```