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
MAE_768_0.25.pt	768	6	0.25
MAE_768_0.75.pt	768	6	0.75
MAE_576_0.5.pt	576	6	0.5
MAE_384_0.5.pt	384	6	0.5
(others)

Usage

pip install huggingface_hub
huggingface-cli download whilethis/NEMESIS MAE_768_0.5.pt --local-dir pretrained/

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

Citation

@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},
}