Release verified encoder-only HighIQS-MAE weights

6ef5ffa verified 13 days ago

2.2 kB

---
license: cc-by-4.0
tags:
- medical
- 3d
- self-supervised-learning
- feature-extraction
---
# ResEncL-HighIQS-MAE

Encoder-only release accompanying [Benchmarking Self-Supervised 3D Medical Vision Foundation Models and Transferability Estimation](https://arxiv.org/abs/2610.17573v1).

Model ID: 752; BaseMAETrainer; final checkpoint at epoch 1000. No downstream fine-tuned weights are included. This is a native PyTorch encoder, not a Transformers AutoModel.

## Status and rights
The encoder weights are released under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/), with team permission confirmed by the author. Please credit the paper and this model repository and indicate modifications. Architecture reference: [MIC-DKFZ/nnssl](https://github.com/MIC-DKFZ/nnssl). The loader is adapted from its ResEncL architecture configuration; the local source snapshot carries CC-BY-SA-4.0, whose attribution and share-alike obligations are retained separately for that code. The weight license does not relicense third-party dependencies.

## Input and usage
Single-channel 3D input, with the recorded pretraining plan using 1 mm isotropic resampling, Z-score normalization and recommended 160-cube patches. Input preparation is external to this loader. The exact training subset definition and preprocessing conventions should be consulted before downstream adaptation.

```python
from load_encoder import load_encoder
encoder = load_encoder('.')
# Provide your preprocessed tensor [batch, 1, depth, height, width].
```

For a synthetic 64-cube loading check: `python load_encoder.py .`. This does not establish clinical validity or downstream performance. See validation.json for strict loading, exact roundtrip and finite-value checks.

Only encoder tensors and necessary configuration are present. Reconstruction decoder, optimizer, training logs, initialization arguments and training sample manifests are excluded. Intended for research; not validated as a clinical diagnostic system. This is an intended-use statement, not an additional restriction on the CC BY license. No later Brier/selection development results are claimed as v1 results.

	---
	license: cc-by-4.0
	tags:
	- medical
	- 3d
	- self-supervised-learning
	- feature-extraction
	---
	# ResEncL-HighIQS-MAE

	Encoder-only release accompanying [Benchmarking Self-Supervised 3D Medical Vision Foundation Models and Transferability Estimation](https://arxiv.org/abs/2610.17573v1).

	Model ID: 752; BaseMAETrainer; final checkpoint at epoch 1000. No downstream fine-tuned weights are included. This is a native PyTorch encoder, not a Transformers AutoModel.

	## Status and rights
	The encoder weights are released under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/), with team permission confirmed by the author. Please credit the paper and this model repository and indicate modifications. Architecture reference: [MIC-DKFZ/nnssl](https://github.com/MIC-DKFZ/nnssl). The loader is adapted from its ResEncL architecture configuration; the local source snapshot carries CC-BY-SA-4.0, whose attribution and share-alike obligations are retained separately for that code. The weight license does not relicense third-party dependencies.

	## Input and usage
	Single-channel 3D input, with the recorded pretraining plan using 1 mm isotropic resampling, Z-score normalization and recommended 160-cube patches. Input preparation is external to this loader. The exact training subset definition and preprocessing conventions should be consulted before downstream adaptation.

	```python
	from load_encoder import load_encoder
	encoder = load_encoder('.')
	# Provide your preprocessed tensor [batch, 1, depth, height, width].
	```

	For a synthetic 64-cube loading check: `python load_encoder.py .`. This does not establish clinical validity or downstream performance. See validation.json for strict loading, exact roundtrip and finite-value checks.

	Only encoder tensors and necessary configuration are present. Reconstruction decoder, optimizer, training logs, initialization arguments and training sample manifests are excluded. Intended for research; not validated as a clinical diagnostic system. This is an intended-use statement, not an additional restriction on the CC BY license. No later Brier/selection development results are claimed as v1 results.