README.md

---
license: cc-by-4.0
pretty_name: LMOD+ (Sample Subset)
size_categories:
  - 1K<n<10K
task_categories:
  - image-classification
  - object-detection
  - image-segmentation
  - image-to-text
tags:
  - medical
  - ophthalmology
  - benchmark
  - multimodal
  - mllm
  - retina
  - oct
  - fundus
configs:
  - config_name: cataract_1k
    data_files:
      - split: test
        path: data/cataract_1k/test-*.parquet
  - config_name: cau001
    data_files:
      - split: test
        path: data/cau001/test-*.parquet
  - config_name: oimhs
    data_files:
      - split: test
        path: data/oimhs/test-*.parquet
  - config_name: origa
    data_files:
      - split: test
        path: data/origa/test-*.parquet
  - config_name: eye_disease
    data_files:
      - split: test
        path: data/eye_disease/test-*.parquet
---

# LMOD+ — Sample Subset

A **1,076-instance sample** of [LMOD+](https://kfzyqin.github.io/lmod_plus/), a large-scale multimodal
ophthalmology benchmark for developing and evaluating multimodal large language models (MLLMs).

This repository is a **preview subset** intended for quickly inspecting the data format, prototyping
evaluation harnesses, and running smoke tests. The full benchmark contains **32,633 instances** across
12 ophthalmic conditions and 5 imaging modalities.

> 📄 Paper: [ACM Transactions on Computing for Healthcare (2026)](https://doi.org/10.1145/3801746) · [arXiv:2509.25620](https://arxiv.org/abs/2509.25620)
> 🌐 Project page: https://kfzyqin.github.io/lmod_plus/

## Quick start

```python
from datasets import load_dataset

# Anatomical recognition with bounding boxes
ds = load_dataset("Euanyu/LMOD-Plus-Sample", "origa", split="test")
print(ds[0]["bboxes"])
# [{'region_type': 'Optic Disc', 'xmin': 0.528, 'ymin': 0.400, 'xmax': 0.651, 'ymax': 0.626}, ...]

# Multi-class disease diagnosis
ds = load_dataset("Euanyu/LMOD-Plus-Sample", "eye_disease", split="test")
print(ds[0]["diagnosis"])  # 'cataract'

# Disease staging + demographics
ds = load_dataset("Euanyu/LMOD-Plus-Sample", "oimhs", split="test")
print(ds[0]["mh_stage"], ds[0]["patient_age"], ds[0]["patient_sex"])
```

Streaming works too, which is useful for the larger `origa` config:

```python
ds = load_dataset("Euanyu/LMOD-Plus-Sample", "origa", split="test", streaming=True)
```

## Configurations

| Config | Instances | Modality | Primary task | Annotations |
| --- | --- | --- | --- | --- |
| `cataract_1k` | 200 | Surgical scenes (SS) | Anatomical recognition | 688 boxes over 15 region types |
| `cau001` | 200 | Lens photographs (LP) | Anatomical recognition | 655 boxes (eye / iris / pupil) |
| `oimhs` | 76 | OCT | Staging + demographics | 508 boxes, 206 masks, MH stage 1–4 |
| `origa` | 200 | Color fundus (CFP) | Binary screening | 400 boxes (disc / cup), CDR |
| `eye_disease` | 400 | Color fundus (CFP) | Multi-class diagnosis | 4-class label |
| **Total** | **1,076** | | | |

## Schema

All configs share a common core:

| Field | Type | Description |
| --- | --- | --- |
| `image_id` | `string` | Identifier, unique within a config |
| `dataset` | `string` | Source dataset name |
| `modality` | `string` | `SS`, `LP`, `OCT`, or `CFP` |
| `image_type` | `string` | Free-text modality description from the annotation pipeline |
| `image` | `Image` | The clean, un-annotated image |
| `relative_path` | `string` | Path to this image inside the raw archives (see below) |

Config-specific fields:

- **`cataract_1k`, `cau001`, `origa`, `oimhs`** — `bboxes`: list of `{region_type, xmin, ymin, xmax, ymax}`
- **`origa`** — `glaucoma_label` (`Glaucoma` / `Non-Glaucoma`), `cdr` (cup-to-disc ratio), `ecc_cup`, `ecc_disc`
- **`oimhs`** — `segmentations`: list of `{segmentation_class, mask}` (retina / choroid); `mh_stage` (1–4);
  `patient_id`, `patient_age`, `patient_sex`, `eye_side`; image-quality flags `low_signal_strength`,
  `signal_shield`, `image_blur`
- **`eye_disease`** — `diagnosis`: `cataract`, `diabetic_retinopathy`, `glaucoma`, or `normal`

### Bounding box convention

Coordinates are **normalized to `[0, 1]`** with the origin at the **top-left** corner. Convert to pixels by
multiplying by the image width and height:

```python
from PIL import ImageDraw

row = ds[0]
img = row["image"].convert("RGB")
W, H = img.size
draw = ImageDraw.Draw(img)
for b in row["bboxes"]:
    draw.rectangle([b["xmin"] * W, b["ymin"] * H, b["xmax"] * W, b["ymax"] * H], outline="lime", width=4)
```

`eye_disease` is a pure classification config and has no `bboxes` column.

## Raw archives

The parquet files contain the clean images plus structured annotations, which covers most use cases. If you
need the original on-disk layout — including the pre-rendered annotation overlays used to produce the paper's
figures — each sub-dataset is also shipped as a tarball under `raw/`:

```
raw/subset_Cataract-1K.tar.gz    600 files    312 MB
raw/subset_CAU001.tar.gz         600 files    202 MB
raw/subset_OIMHS.tar.gz          716 files     78 MB
raw/subset_ORIGA.tar.gz          800 files   1403 MB
raw/subset_eye_disease.tar.gz    400 files     73 MB
```

```python
from huggingface_hub import hf_hub_download
import tarfile

p = hf_hub_download("Euanyu/LMOD-Plus-Sample", "raw/subset_OIMHS.tar.gz", repo_type="dataset")
tarfile.open(p).extractall("./lmod_raw")
```

Each sample folder holds the clean image, an `information.json` (or `KEYINFORMATION*.json`), and an
`annotated/` or `metadata/` subtree with overlays. The `image_path` field inside every JSON is relative to the
subset root, so `subset_OIMHS/` + `100_30/visualization.png` resolves directly.

## Tasks

LMOD+ supports four task families. This subset exercises all of them:

1. **Anatomical recognition** — localize structures (cornea, pupil, iris, optic disc/cup, retina, choroid,
   surgical instruments) via bounding boxes and segmentation masks.
2. **Disease screening** — binary detection, e.g. glaucoma vs. non-glaucoma in `origa`.
3. **Disease staging** — severity grading, e.g. macular hole stages 1–4 in `oimhs`.
4. **Demographic prediction** — infer patient age and sex from the image (`oimhs`).

Reported zero-shot performance in the paper is far from saturated: the strongest of the 24 evaluated MLLMs
reaches roughly **58% accuracy** on disease screening.

## Images are unmodified

Image bytes are embedded **bit-exact** from the source pipeline — no resizing, re-encoding, or recompression.
Note that this means `origa` inherits the pipeline's PNG conversion of originally-JPEG fundus photographs, which
is why that config is disproportionately large (~700 MB for 200 images at 2518×2048).

## Source datasets and attribution

LMOD+ is an aggregation of publicly released ophthalmic datasets, re-annotated with a unified multi-granular
schema. Under CC BY 4.0 you must credit LMOD+; you are **also** responsible for honouring the terms of each
upstream source, which are not superseded by this repository's license.

| Sub-dataset | Modality | Upstream source |
| --- | --- | --- |
| Cataract-1K | Surgical scenes | Cataract-1K cataract surgery dataset |
| CAU001 | Lens photographs | See the LMOD+ paper, §Datasets |
| OIMHS | OCT | OIMHS — OCT images with macular hole segmentation |
| ORIGA | Color fundus | ORIGA-light retinal fundus dataset |
| Eye Diseases | Color fundus | 4-class fundus classification collection; see the LMOD+ paper |

If you redistribute or build on this subset, cite both LMOD+ and the relevant upstream dataset.

## Demographics and privacy

The `oimhs` config includes `patient_id`, `patient_age`, `patient_sex`, and `eye_side`. These are carried over
from the public upstream release and are required for the demographic-prediction task; `patient_id` is a
pseudonymous integer from the source dataset and enables patient-level grouping so that images from the same
patient do not straddle a train/test boundary. No direct identifiers are present. All internal file paths from
the annotation pipeline have been stripped from both the parquet files and the raw archives.

## Known issues

- **Region-label typos.** Two boxes in `cataract_1k` carry malformed labels: `case5013_14` has a `pupil1` box
  alongside its `Pupil` box, and `case5014_18` has `cornea1` alongside `Cornea`. These are preserved as-is so
  the data matches what was evaluated in the paper. Filter or normalize them if your pipeline is
  label-sensitive.
- **Partial anatomical coverage in `cau001`.** 50 of 200 samples annotate only the iris and pupil, without an
  enclosing `Left Eye` / `Right Eye` box.
- **Class balance is by construction.** The per-class counts in this subset (100 per diagnosis, 19 per macular
  hole stage) were sampled to be balanced and do **not** reflect prevalence in the full benchmark or in a
  clinical population.

## Relationship to the full release

This subset is drawn from the full LMOD+ benchmark (32,633 instances; additionally covering Harvard FairSeg,
CatDet2, REFUGE, IDRiD, and G1020). It is intended for format inspection and prototyping — **do not report
benchmark numbers on this subset**, as the sample sizes are far too small for meaningful comparison and the
class balance is artificial.

## Citation

```bibtex
@article{10.1145/3801746,
  author     = {Qin, Zhenyue and Liu, Yang and Yin, Yu and Ding, Jinyu and Zhang, Haoran
                and Li, Anran and Campbell, Dylan and Wu, Xuansheng and Zou, Ke
                and Keenan, Tiarnal D. L. and Chew, Emily Y. and Lu, Zhiyong
                and Tham, Yih Chung and Liu, Ninghao and Zhang, Xiuzhen and Chen, Qingyu},
  title      = {LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing
                and Evaluating Multimodal Large Language Models in Ophthalmology},
  year       = {2026},
  issue_date = {July 2026},
  publisher  = {Association for Computing Machinery},
  address    = {New York, NY, USA},
  volume     = {7},
  number     = {3},
  url        = {https://doi.org/10.1145/3801746},
  doi        = {10.1145/3801746},
  journal    = {ACM Trans. Comput. Healthcare},
  month      = jun,
  articleno  = {52},
  numpages   = {38}
}
```

The preliminary version of this benchmark appeared at NAACL 2025:

```bibtex
@inproceedings{qin-etal-2025-lmod,
  title     = {{LMOD}: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models},
  author    = {Qin, Zhenyue and Yin, Yu and Campbell, Dylan and Wu, Xuansheng and Zou, Ke
               and Liu, Ninghao and Tham, Yih Chung and Zhang, Xiuzhen and Chen, Qingyu},
  booktitle = {Findings of the Association for Computational Linguistics: NAACL 2025},
  month     = apr,
  year      = {2025},
  address   = {Albuquerque, New Mexico},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/2025.findings-naacl.135/},
  doi       = {10.18653/v1/2025.findings-naacl.135},
  pages     = {2501--2522}
}
```