HY2333/MMPD_Bench

MMPD-Bench

Dataset Summary

MMPD-Bench is a polarimetric imaging benchmark for learning mappings from Mueller matrix observations to polarimetric decomposition modalities. Each sample contains a channel-first Mueller matrix tensor and a channel-first target tensor with six Lu-Chipman reference modalities.

Current Hugging Face release status:

• Uploaded: external waveplate test data at 633 nm.
• Uploaded: external spectral test data at 610, 650, and 690 nm.
• Uploaded: healthy bone cell train, validation, and test splits.

Because the waveplate tensors are 200 x 200 while the healthy bone cell and spectral tensors are 256 x 256, the data is published as separate configs:

• healthy_bone_cell
• external_waveplate
• external_spectral

Task Definition

The task is modality fission from a Mueller matrix tensor to six polarimetric target modalities. It is not a segmentation or classification dataset.

• Input: Mueller matrix tensor, shape [16, H, W], channel-first.
• Output: target modality tensor, shape [6, H, W], channel-first.
• Target channel order: D, Delta, eta, theta, psi, R.

Data Sources

This release contains healthy bone cell data from polarization_v2 and external test data from polarization_v3:

• Healthy bone cell data: source-provided patch splits from 53 sample folders.
• Waveplate data: hwp633 and qwp633, measured at 633 nm.
• Multi-wavelength spectral data: selected wavelengths from mwl_530_690, currently 610, 650, and 690 nm.

File Structure

MMPD-Bench/
├── README.md
├── data/
│   ├── external_waveplate-00000-of-00001.parquet
│   ├── external_spectral_610-00000-of-00001.parquet
│   ├── external_spectral_650-00000-of-00001.parquet
│   ├── external_spectral_690-00000-of-00001.parquet
│   ├── train-00000-of-00094.parquet
│   ├── validation-00000-of-00012.parquet
│   └── test-00000-of-00011.parquet
└── metadata/
    ├── acquisition_info.json
    ├── channel_order.json
    ├── healthy_bone_cell_manifest.jsonl
    ├── healthy_bone_cell_manifest_summary.json
    ├── parameter_ranges.json
    ├── schema.json
    └── split_summary.json

Tensor Schema

Common columns:

{
    "sample_id": str,
    "source_id": str,
    "split": str,
    "subset": str,          # healthy_bone_cell, waveplate, or spectral
    "specimen_type": str,   # healthy_bone_cell, waveplate, or spectral
    "wavelength_nm": int | None,
    "source_path": str,
    "mueller_shape": list[int],
    "target_shape": list[int],
    "mueller": array,       # float32, channel-first
    "target": array,        # float32, channel-first
}

Waveplate-specific columns:

{
    "plate_type": str,      # hwp or qwp
    "angle_label": str,     # e.g. 0deg, n22, p45
    "angle_deg": float,
}

Patch-based columns for healthy bone cell and spectral rows:

{
    "patch_id": str,
    "target_encoding": str, # png_uint8_normalized_to_float32_0_1
}

Current tensor shapes:

• healthy_bone_cell: mueller = [16, 256, 256], target = [6, 256, 256].
• external_waveplate: mueller = [16, 200, 200], target = [6, 200, 200].
• external_spectral_*: mueller = [16, 256, 256], target = [6, 256, 256].

Channel Conventions

Mueller channel order:

M11, M12, M13, M14,
M21, M22, M23, M24,
M31, M32, M33, M34,
M41, M42, M43, M44

Target channel order:

D, Delta, eta, theta, psi, R

Local source files may use names such as Ita, ita, or Eta; the public channel name is normalized to eta.

Physical Parameter Definitions

Mueller matrix elements are generally expected to lie within [-1, 1] after normalization. In measured data, small deviations outside this range may occur because of acquisition noise, calibration differences, numerical processing, or normalization error. Users should inspect the value distribution for their split and apply task-appropriate preprocessing before training, such as clipping, standardization, or normalization based on the training set.

The target tensor follows this channel order and nominal parameter range:

D, Delta: [0, 1]
eta, R: [0, pi)
theta, psi: [-pi/2, pi/2)

Important encoding note:

• Waveplate target arrays are stored from the source .npy files as float32.
• Healthy bone cell and spectral target arrays were converted from grayscale PNG files to float32 values normalized to [0, 1]; see target_encoding.
• Mueller matrix tensors are stored as measured/processed values, not forcibly clipped to [-1, 1].

Optional Mapping From Grayscale Targets to Physical Ranges

For rows whose target_encoding is png_uint8_normalized_to_float32_0_1, the stored target tensor is a normalized grayscale representation in [0, 1]. To map these values back to the nominal physical parameter ranges used in the paper, apply:

import numpy as np

TARGET_CHANNELS = ["D", "Delta", "eta", "theta", "psi", "R"]


def normalized_modalities_to_physical(target, channel_axis=0, clip=False):
    """Map normalized grayscale modalities to nominal physical ranges.

    Use this only for targets encoded as
    ``png_uint8_normalized_to_float32_0_1``. If a split already stores physical
    Lu-Chipman values, do not apply this conversion again.
    """
    target = np.asarray(target, dtype=np.float32)
    values = np.moveaxis(target, channel_axis, 0)
    if values.shape[0] != 6:
        raise ValueError(f"Expected 6 target channels, got shape {target.shape}")

    g = np.clip(values, 0.0, 1.0) if clip else values
    physical = np.empty_like(g, dtype=np.float32)
    physical[0] = g[0]                      # D: [0, 1]
    physical[1] = g[1]                      # Delta: [0, 1]
    physical[2] = np.pi * g[2]              # eta: [0, pi)
    physical[3] = np.pi * (g[3] - 0.5)      # theta: [-pi/2, pi/2)
    physical[4] = np.pi * (g[4] - 0.5)      # psi: [-pi/2, pi/2)
    physical[5] = np.pi * g[5]              # R: [0, pi)
    return np.moveaxis(physical, 0, channel_axis)

The inverse mapping is:

D_gray = D
Delta_gray = Delta
eta_gray = eta / pi
theta_gray = theta / pi + 0.5
psi_gray = psi / pi + 0.5
R_gray = R / pi

Visualization note: after applying this optional physical-range mapping, use the nominal physical ranges for color scales when comparing samples or models: D/Delta in [0, 1], eta/R in [0, pi], and theta/psi in [-pi/2, pi/2]. Per-sample min/max color scales are useful for inspection, but they can make cross-sample or cross-modality comparisons visually misleading. The helper script scripts/test2.py demonstrates both normalized targets and physical targets with fixed physical colorbar ranges.

Reference Label Generation

The target modalities are generated using Lu-Chipman decomposition from measured Mueller matrices. They should be interpreted as physics-solver reference labels for benchmarking surrogate models and physics consistency, not as direct human annotations or absolute biological ground truth.

Splits

Split	Config	Subset	Samples	Shape	Notes
train	healthy_bone_cell	healthy_bone_cell	6006	[16, 256, 256] -> [6, 256, 256]	94 shards
validation	healthy_bone_cell	healthy_bone_cell	713	[16, 256, 256] -> [6, 256, 256]	12 shards
test	healthy_bone_cell	healthy_bone_cell	643	[16, 256, 256] -> [6, 256, 256]	11 shards
external_waveplate	external_waveplate	waveplate	18	[16, 200, 200] -> [6, 200, 200]	633 nm HWP/QWP
external_spectral_610	external_spectral	spectral	165	[16, 256, 256] -> [6, 256, 256]	610 nm
external_spectral_650	external_spectral	spectral	165	[16, 256, 256] -> [6, 256, 256]	650 nm
external_spectral_690	external_spectral	spectral	165	[16, 256, 256] -> [6, 256, 256]	690 nm

Benchmark Protocols

Evaluation configs:

1. Healthy bone cell benchmark: use config healthy_bone_cell, splits train, validation, and test.
2. External waveplate evaluation: use config external_waveplate, split external_waveplate.
3. External spectral evaluation: use config external_spectral, then evaluate external_spectral_610, external_spectral_650, and external_spectral_690.

Loading Instructions

Install the Hugging Face datasets package:

pip install datasets

Load one external spectral split:

from datasets import load_dataset
import numpy as np

ds = load_dataset(
    "parquet",
    data_files={
        "external_spectral_610": (
            "hf://datasets/HY2333/MMPD_Bench/"
            "data/external_spectral_610-*.parquet"
        )
    },
    split="external_spectral_610",
)

row = ds[0]
mueller = np.array(row["mueller"], dtype=np.float32)
target = np.array(row["target"], dtype=np.float32)

print(row["sample_id"])
print(mueller.shape)
print(target.shape)

Load via dataset config:

from datasets import load_dataset

healthy = load_dataset("HY2333/MMPD_Bench", "healthy_bone_cell")
spectral = load_dataset("HY2333/MMPD_Bench", "external_spectral")
waveplate = load_dataset("HY2333/MMPD_Bench", "external_waveplate")

Note: in some environments, streaming reads of large nested Parquet tensors can trigger a PyArrow shutdown issue after successful iteration. For a stable smoke test, use non-streaming loading on a single split as shown above.

Ethics and Limitations

The current public release focuses on healthy bone cell and external physical/spectral evaluation data. Diseased biological samples are not included in this release.

The targets are Lu-Chipman reference outputs. Evaluation should be interpreted as agreement with a physics-solver reference and related physics consistency, not as proof of absolute biological ground truth.

Measured Mueller matrix entries may be slightly outside the nominal [-1, 1] range. This is expected for real acquisition pipelines; users should decide whether to clip, standardize, or otherwise normalize values according to their training protocol.

License

This dataset is released under CC BY-NC 4.0.

Citation

TODO: Add the MMPD-Bench paper citation and BibTeX entry.

Contact

TODO: Add maintainer contact details.