Datasets:
Upload external_waveplate pilot: README.md
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README.md
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| 1 |
---
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+
pretty_name: MMPD-Bench
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+
license: cc-by-nc-4.0
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tags:
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- polarimetry
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- mueller-matrix
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- biomedical-imaging
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- healthy-bone-cell
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- scientific-computing
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- parquet
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task_categories:
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- image-to-image
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configs:
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- config_name: external_waveplate
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default: true
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data_files:
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- split: external_waveplate
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path: data/external_waveplate-*.parquet
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---
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# MMPD-Bench
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## Dataset Summary
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TODO: Describe MMPD-Bench, the scientific motivation, and the intended benchmark
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use. Mention that the dataset contains paired Mueller matrix observations and
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Lu-Chipman reference decomposition outputs.
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Current pilot upload: `external_waveplate` only.
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## Task Definition
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TODO: Define the task as modality fission from a Mueller matrix patch to six
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polarimetric target modalities.
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- Input: Mueller matrix tensor, shape `[16, H, W]`, channel-first.
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- Output: target modality tensor, shape `[6, H, W]`, channel-first.
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- Target channel order: `D`, `Delta`, `eta`, `theta`, `psi`, `R`.
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## Data Sources
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TODO: Describe healthy bone cell samples, waveplate samples, and
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multi-wavelength measurements. Include acquisition context and de-identification
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statement if applicable.
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## File Structure
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```text
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MMPD-Bench/
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├── README.md
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├── data/
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├── metadata/
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├── preview/
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└── raw/
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```
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TODO: Replace this with final shard counts and file sizes after conversion.
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## Tensor Schema
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Each row represents one paired patch/sample:
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```python
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{
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"sample_id": str,
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"source_id": str,
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"split": str,
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"subset": str, # healthy_bone_cell, waveplate, spectral
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"specimen_type": str, # healthy_bone_cell, waveplate, spectral, unknown
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"wavelength_nm": int,
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"mueller_shape": list[int],
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"target_shape": list[int],
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"mueller": array, # float32, channel-first
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"target": array, # float32, channel-first
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}
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```
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TODO: Confirm whether arrays are stored as physical values, normalized values,
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or both. The `external_waveplate` pilot shard currently stores tensors with
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shapes `[16, 200, 200]` and `[6, 200, 200]`.
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## Channel Conventions
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Mueller channel order:
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```text
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M11, M12, M13, M14,
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M21, M22, M23, M24,
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M31, M32, M33, M34,
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M41, M42, M43, M44
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```
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Target channel order:
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```text
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D, Delta, eta, theta, psi, R
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```
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TODO: Document the mapping from local filenames such as `Ita`, `ita`, or `Eta`
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to public channel name `eta`.
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## Physical Parameter Definitions
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TODO: Define every target parameter and confirm ranges.
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```text
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D, Delta: [0, 1]
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eta, R: [0, pi)
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theta, psi: [-pi/2, pi/2)
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```
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## Reference Label Generation
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The target modalities are generated using Lu-Chipman decomposition from the
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measured Mueller matrices. They should be interpreted as reference solver
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outputs for benchmarking surrogate models and physics consistency, rather than
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direct human annotations or absolute ground truth for biological tissue.
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TODO: Add decomposition implementation details, preprocessing, and any numerical
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stability handling.
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## Splits
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TODO: Fill exact counts after conversion.
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| Split | Subset | Samples | Notes |
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|---|---:|---:|---|
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| train | healthy_bone_cell | TODO | Primary training split |
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| validation | healthy_bone_cell | TODO | Model selection split |
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| test | healthy_bone_cell | TODO | Clean healthy bone cell test |
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| external_waveplate | waveplate | 18 | External generalisation at 633 nm |
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| external_spectral_610 | spectral | TODO | External wavelength test |
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| external_spectral_650 | spectral | TODO | External wavelength test |
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| external_spectral_690 | spectral | TODO | External wavelength test |
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TODO: State whether healthy bone cell splits are specimen-level or patch-level.
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If patch-level, describe leakage risk as a limitation.
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## External Test Sets
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TODO: Describe waveplate and spectral external test sets, including wavelength,
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sample source, and intended usage.
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## Benchmark Protocols
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1. Clean healthy bone cell test: train on `train`, tune on `validation`, report on
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`test`.
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2. Noisy healthy bone cell robustness: evaluate the test set with
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`sigma_noise = 0.1 * sigma_pixel`.
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3. External generalisation: train only on healthy bone cell training data and
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report on `external_waveplate` and each `external_spectral_*` split.
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## Metrics
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TODO: List recommended metrics for each target modality and any aggregate score.
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## Loading Instructions
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```python
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from datasets import load_dataset
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ds = load_dataset("YOUR_ORG/MMPD-Bench")
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print(ds)
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print(ds["train"][0].keys())
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```
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```python
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import torch
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row = ds["train"][0]
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mueller = torch.tensor(row["mueller"], dtype=torch.float32)
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target = torch.tensor(row["target"], dtype=torch.float32)
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```
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## Ethics and Limitations
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TODO: Add de-identification statement, biological ground-truth limitation, and
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recommended use boundaries.
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## License
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TODO: Add final dataset license and any access restrictions.
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## Citation
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TODO: Add paper citation and BibTeX.
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## Contact
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TODO: Add maintainer contact details.
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